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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TrOCR/Inference_with_TrOCR_%2B_Gradio_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5Z682OrQ6br"
      },
      "source": [
        "## Set-up environment\n",
        "\n",
        "We install 🤗 Transformers, which includes many awesome models, including TrOCR by Microsoft Research."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "d9JLY5fJ2o4R",
        "outputId": "754d60ec-5b74-444a-e19d-49ebb6f6a087"
      },
      "source": [
        "!pip install -q transformers"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 4.0 MB 9.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 895 kB 61.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 596 kB 50.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 77 kB 6.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 46.6 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JB0lrqtE56xs"
      },
      "source": [
        "## Load image\n",
        "\n",
        "Here we load an image from the [IAM Handwriting Database](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database), on which TrOCR was fine-tuned."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
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        "id": "zcPm6rpT55zE",
        "outputId": "a2c0e345-be82-4fc6-af56-b21c632aef89"
      },
      "source": [
        "import requests\n",
        "from PIL import Image\n",
        "\n",
        "url = \"https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg\"\n",
        "image = Image.open(requests.get(url, stream=True).raw).convert(\"RGB\")\n",
        "image"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=1024x100 at 0x7F1B97DB1910>"
            ],
            "image/png": 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0QGKJZJuUO9aQRCLB87xKpRIEIRaLEUI0Gg3lgIlMuV9q7c4FW4QBMeOizAne9EpkAsQmGbgrvJvkQz6hTG6odhRnU5Y5/Z5+yd6WdjhVoljNhHVESNZIPoHHIFcBoIq35I3GNRCI49nIafNykesHuMtPrGbkLjQ6b8nYXMm574sv3zUGQLKur3yOXZ1I1BuSM1dzlYT3tT1TMiVTMiVT8sHKGHMyGXuEs1/Sz5IKu1TyASkanggQAINxMpm0WCzBYNBoNCYSiWQyqVaruaw5mTD4mzYgGo3S0El8SWMJJPRibjzzPBlPbWBRvuQn9FAfF6SKjLVS8iB6T/aGHEMOkbyaJKxQMgqEQR4iEzNAr5kYx7CS+1wxJ+MKyWNBJGPzxLOvTDufY4hMucGdHJNXlHXX0PaMO+6S78X3zQPA3pCOL0XektHMHcdxpxydQuw1dNbBJUJpP3iWTCbD9/l0KskqE/Mr7RMoNuOKxLNEmD7PvT/JgcjsZfkaz6442huslsVO+2sluYtd8jqSx42774ljlU/2LSSvdiXCtiS3YRNcPG6DJZIvBmBc9tq73m1KpmRKpuT9k4n3vSl5v0UuMpFtbEyqhFFDx0OCa3PVAMkJCsjOQu1UKhWLxWKxWEtLS1NTE4W2iUQiN7UL2wAJsKAs7VyCBJdNVwKKETeWnE0YCys3lv3PUgVwW1CYJCh8gtmZD3hJ2s8xIsEZ+UAY/TcXhb8r1GN/hYvZfCzsQyUeCbZhuW9KsgoA2xK8EYJcE4mEx+MRBMFgMGg0GlEUaXpEdqQmQLqS3s7tNInks7zmk3zjRUNNJEBNQgCj0EpSUE8cS3DimSz4+IYydiRKKXsf9m5UMZD0Hts/7KSi34w7bXKFUtQk70spOmTsNMvNQsP+UPI4MevpkixnSmljryR55tvVCZ9Tf4C+ywQLioyd/Ox7kZzVJHlfcUI1hl0mV/gK7JiyW8GkRPI6ubdie+bqHjElUzIlUzIlHyGRU8QvZIUQwvO8JO0jPQ/A4aZnEkvbpTeVnN+4LTg5gIDxePzo0aP79+8vLCysrKxEzYF4PK5SqVjLMXsfpVJJLbJiVlcBgGBDF9AYoA3qLsA9qXrD/pxeTM3S7PmHa6Ah4Ol4EO0owiAGjmESSyQXV7FAVgIgWBiH79n2s/iPXj8uhp5YJEgxV2irhAnTgPJj0zVSSAo6ezAYbG9vF0Wxvr5eq9Wy2hSlY2GYEokEGQsx8d986Q6F/LETk+qH3LdGG5C1iY1fxzvm6wdaIVvC4cH85LLKkpDNhkly+DnprKBD2ET+mUwmmUzy2cIXJGfFUUWO9m0+nJdP2HVNcizTkklOGEszz8SQkJxab/RXPBMjQec2/ZBvFN67SIA+x6hV3Fj1SfKCtCX0g0QBkLSfdlfu67Mi8bDRn0z8vuxwT3zxuB48iX4ieS/JZpLbCVMyJVMyJVPy8RMuGo2ymJKSNLixjGR62oGlI+ZEcAKgSOCpKIo8zyeTSUEQFAoFz/Nut7ugoGDt2rVyuTyZTG7atOm+++6z2+2pVMpgMGQymWg0yiY1F5jM6OO+QCwWUygUUA+EbDJvwuQjJ4RQV0A6nVYqlcFgUKvVarXaUCiUTCa1Wq1KpYpEIuwpy/O8XC6XyWS0ABno2gCFlKpET1lBEFKpFKIa6Dfs0wEQJYCSBRDcWKHX8Ey5A6Dq3DoAuThGzFr3g8FgUVFROp1OJBIIttbr9S6XS6vV0o5iy7olk0k0gGcCA8jYOg+02YCziUTCYDAkk8lwOGw0GjmO8/v9oigmk8m77roLjfziF7946623ZjIZvV5PcwEBHGNcuLFuFsKEn9I3Aj7GD+VyeTwex8UajSaZTMZiMUyh8Sd6HkzzroqEBDCBK8/qMJh17BRlgTj+FIvF1Gq1TCaLRCKiKKpUKp7nEf6LhYCw6VgshrcTRRFV2CjiTyQSHMchsVImk8G0RNEMTAyQ6PBQj8djNptDoVAmk8H0pqoFnGwSXMiiwHEVhnEVgNzrc7Epnc/5JupksWa+6/PtD3TeUtMGJF+6YYVCAVYhyVoQRMbHKNG92afnU7RYSM3lrxuQr/8nmLfjAn1MHpIzLpNVjD/qOoCkAyXdOyVTMiV/Tcm37iYwlLyvz/2g9rdrtf/ku88EdX7YH9K/yqlrnjVr8dkCVRKgwPN8PB6XPIDFi5L2Acew1nqj0RgIBLRabTweFwShsbFRr9cjCBiQTq1WU2iV29xcQbJ5FjFzWf4Pa7ilIDuTyVgslnQ67fP5OI4zm82EkFAopFAoKK6FULArjqUQkGyEsaQfABdyoRIL6+lL0Vaxhl7a/4Sx6bKvTxlNtJ0sBqWP4BibIiqsCYKgVqsByv1+f2FhIbQy9nF0jNj70KejLhX9E7Vwa7VaYCyZTKbVagGYVCqVz+d74YUXkslkJBKxWq0ajebkyZNLly7FfdgR5HkehSAIw/inPUYTXGJYaUwqpgrHcUDPgLaAxfmmyrjy3gEoHU1uLJsL0yYWi+l0Ovi40ul0LBbDNRqNBvokx3FKpVIUxXQ6DdUUmmQikYDygNsWFhYCm0JT1Wg0hJBQKKTVajEWSKQLRcJqtbLrjoYak5ylRF9H0m/cWMO25AXpNBCZYg74IUv1IeNtPe+l869CJFQrkvWo5Ls+V7HHKwhM2lzJqqTCfsP2GJefEXTVIqnHMoH+IF47d8qUTMmUTMmUfGzkf5Ovs8ZdngmKJWPZDmSsTUUCbbkcZi2weDKZpFbes2fPJhKJRCJhNBqnTZtGCInH43AUJJNJgCGByYBOGFZPrtDrM2NrDEsMY/TIBxiCKd1gMESj0Xg8DuTKMZQbkoUC6AphLFdeYPKys+g8FyGRsQpSLhTgxyaapG0WmOQ8rAEyt6AYVVEkYwFxOp3Qu5xOZzqdrqio8Hg8TqfTYrHwTGwG/ZcNBmXfjtaZYtUP6m+BHwambugVFovl7NmzHMcB1+7du9fpdPr9/rVr19JgburigMIpwS70BakjCN4PMetvAQjG07VarUajyWfWvYYiSZvIznMJ1MOfDAYDWphIJLRarc1mw1/j8TiSgSaTSZ/PhwAYnuddLlcwGBwdHXW73aIomkwmqE9er1etVqNqHn4ll8s1Gg16RqFQoHNisRhqe0Mj4rORMOzAEWZ+5ip7kg8CQ3XL/Sv7J3zIrdycD33+dVCppF6ByLgux72ernr6FnRnkOB+yXQleZRDyX8nfmsJcJ/gSj4n/InPJkWQtITPyf/zyRFWL5rSgqZkSqbkkyy554tcck7TQ46a4UUmgwRrJpRAB/YoYm2NXJY1lEqlwEM4ceIETqlFixbp9fp0Oh2Px+VyeSaTkcvlkvzlPJO8gow92OhzxWymfDIWo9MwXxzkaH80GgWPIhKJ6HQ6ODSUSiV7cEr4yiR7fgDiA6qywEJkYj1p3QMA3FykJUEMErTE/glDIDLs/1wtiHYIn8Mtxj2tVivHcW+88caTTz4ZDoe///3vL1iwAM4WOrhcDuFHcn+RiQGg11PHi1wuj8ViaFs0GsX1nZ2dAwMDLpfLZDIRQnbt2qXT6S5fvrx27VqW1ARVCqiaTjAKrQQmSAO8F9ppKCkQiUT0er2YDSed4IzPB4AmCwvY6/OBP3ZMQ6GQRqPR6XSU2+PxeNLptMPh8Hq9giD4fL4zZ86cOHGio6MjlUopFAosFkwkFE4WBMFqtcZiMb1eX1FRYTab9Xp9Y2Pj4sWLSZbWkslkVCqVwWDwer1QNsCwohEsaJvAlH7LfRcJupV0IJdjTs69Vb7+Gbf/3xXm5srVAdlxl9i4Am8SVfvptJRsd1QkpLhxp9PESkLuNRNfSZ9LdwYydp9kW0L//aQpAO+qjE3JlEzJBy5TavnVyWT7Ld/5IgepmoX7uRYyFnznZgVhQTPJocMiuBNAM5PJeL3erq4uk8kkCMKNN97IcRzKgYmimEwmjUYjqM8khzkDgC452AiThpK+BT2MKWAl2SQtlBfkcrn2799fXFw8ffr0srKyQCCAKALJe9G78UykI/6aSqVorAI1xdGH5mKFXHiNbwQmNaTkSmp6lLggcjufYyrpEgaAiqIYjUaj0eiBAwdGRka8Xm9bW1tZWZnNZpPgFdoAiR5FmwQNja3mRmOsoT4BkQPNu1yud955x+PxEELAafF4PDabzWKxQM2jegU7W/ix0QhittIzjfNmNZDu7u5AIBCLxZqammAITyQSqVRKpVK9y1J4b0IdF7njRf9LmJkATA+ifzKZ1Gg0MpnMZDL19fU99dRTp0+fBl6Px+ORSMRsNsfjcSioJEsZT6fTyWQyGo16vV6e5wcGBkRRDIfDhYWF06dP//znP28ymRwORzweT6VSJpMJpH9QgERRZF1GIuOjkCB+tgCZ5AP7Xvn2HXYiSYaVXsDO89zl8D4JdRnRhYldQsjDxWeXMMfo5+zmMAUlp2RKpmRKpuSjLnIJZqVCLYWUgEEYEMCehfSAJGMhHUVCsGUKghAKhS5cuNDb22s0Gh0Ox+zZs0FiRowsWOlsZVnC8EDQqnGRFsme6xSSsl/CpAoYRAjRaDR79+5ta2s7dOhQeXn52rVrHQ4H4iNFUWSrHFCFR2SidenbsZCCRTZCNjkp25kSiE8YICLJl89egF+xpGTJQLDCmgBZ0Wq1kUhkdHRUo9EYDAZEXKBPqO5B0TbrssgVkcmkRDVGcFHwV3wWBKG1tbW7uzuZTJaWlsJOjzDrwsJCmgaUams0doJVb9h5SLJEMkII9JBEIvHMM894PB6NRtPQ0GC1WuFcGrfZtPET/HXc/r/Cy7icIHV2ZlqtVgTjRqNRk8mkVCp7enpGRkb27Nmzb98+p9NJCNFqtQaDAaqy0WgkhCBVriAIKpVKpVIhsrygoADqmUKhsFqtmUzmwoULTz/9dG1t7ZYtWywWC2hFtCXUr8K+DsvRHxepSz7wTGosMhboj9tLEk2YygST/Er6eeL7jNsSCD+2xhwdr3wKAKvPc1nPCa0qmNtsiVN03M9X8o65/cnOonHfix0gMjZBLTtMn0yNJbf38s3YKZmSKfmgZGo9Xp1Mtt/ynS//a4sVmMTn9CDJPciRRYQCNQofWWGfoVarwdwQRTESiVy8eDEcDtfU1KxYsYKSmMEDQQ5Q0CQoO5xjCDz5OoLSxAmDbHgmNyg4FbjnwMDAX/7yl8HBQa/X6/f7tVrtypUrtVotW02MvjjHcaD68Ew2cbYrxSz7SMy6IHI7YVyAxTEMH0nX4V/qrMDFlPzD9jYZGwOQ2zOEkNHR0ZGRkXA4nEqltFrtzJkzeZ73+XxGo1HyUNwEwdBs43EBG49BtRFCSCqVgm1bFMVoNIrw37feequnpyeZTKZSqXA4LAhCYWGhxWKZOXMmGU+lFLIBD5TYQLuIKm9UOfT7/U6n85133gkGgzabzev16vV6VJIed4ZcW2E7jWN8X7m6Ex2XeDxuMpl0Ol0ymTx//vwf/vCHjo4Oq9U6NDSkVqvT6bROp5s7d65SqbTZbNOmTTMYDDqdjhCCjgV3bmBgwO/39/f39/X1uVyuZDIJxenEiRN9fX0LFy4sLi5Gx2Is4EZgcaqYzdrEvgv9TJcPndgSnZ+M3T7Y+UYhJjfWvUAYn5WQk//nryMUJbOvMEFL6KixLccGJVnUkHz9Oa4V4Epam0/Dl4gE6NMm5TL+c6/85EjuhJySKZmSKflkSu75IqfZbOiZx4/N54MjEBgUKQhZ9C/Buyw2giACGJZ4nucjkYjJZKqpqVm0aFEsFkMl4EwmYzKZ9Ho9m4ycz6aJxG1hHx1XKEakhjpRFGmmTiGbIwUw/ciRI62trVqtlhDidrtBIidMkCv7FmI2nz3Jqjr09Vl+DqsAsHoUC4NYyMi+I+18CRznsglwuGzyR9oePpv5h2O8BJL706fIZLLBwcFQKMRxnMViqaqqSqVSlGolMvl2cFuqaYg5QsbagwGM2M5BDHcwGDx27JhKpTKbzT6fT6FQaDQahUJRUlJit9vFbHJ9+hRY7mlGS5osH1Gt6F5cg6ysHo+nt7cXOTRh+E8kEoiOvYpjPt9P8gEmdoDYy9hpT7/huP9Js6tUKkdHRy9dunTy5MmWlpZMJhMIBKLRqNVqVSqVCxYs+PSnPw12EMdxOp0OHB4EqGAV1NbWQh9wOp3Hjx9vbm52Op0ajcZkMg0PD3d3d0+fPl3I1qygsTTswEnmCW2/BOJLOkTympK3E8cyztkJjw90M8nt578OJKUOK/qNOJ62zLaKakps4ISk/fQb1vMpWfL0cWwXXSthg/IJMy1hQJEA32v+9CmZkimZkin5qIucYlngOQo6CWNSgm01k8kgXQ/NqS9mOTPUfIifi9n85TiNAHT8fv8f//hHr9dbXV29cuVKm80GcohWq1UoFH6/HwxynGG4bTKZJFn6B5uHnm0wmsdxXCKREAQBGUXRjHQ6bTAYRkZG9Hq9TqfzeDyXLl167LHHkBYdrgydTtfX11dUVIRc8mgDbgK8hcuoH0AShiuOjZYG4Zt1XEiOYXGsETGdTodCIZ1OBwgrimI8HrdardFoNBwOv/TSSyqVqra2dtq0achZFAwGdTodx1QioyOFt6akeUIIAknhdREEYfXq1UuXLk2n0xaLBaiUIiHWPko/CzlFFSQoinZIJpMxGAw8z7vd7kAg8Oijj2o0Gr/fb7FYjEYjbNWJRGLFihV4OzI21ATzBLeleibVjlA/GCEEXq9XLper1erTp0/7fD5RFKE2iKIIlrxMJotGo2q1Ghb0SCSiUCgUCgVyzsLFRAhRqVS4mL67BCvz2TS4ucLCPtoPFF3lIktER/h8vhMnTvzwhz/UarXDw8M2m00UxRkzZjQ1Na1du7aysjKTySgUCovFAo9KMBhEmiafzxeJRMrLyy0Wi8fjUavVDoejurr6b//2b8+dO/fwww+PjIzY7fZf/OIXRUVFa9asyWQybrdbpVJR9ZuqixQKs9g0n2Gegma65GkEPG6l1WqxQOgCRHlvq9WaO+dZCE6/l/TwuArVBDKuZjLur3Jfkx2ycV8czaapkPmctMK0b+k17GIXGQVeZIJbOMbHRbsFgvobrMpBf8sqUSITjTBub0hmoKSX3rtMVsfO92iUCYd1A+o93WmxkWJbQNpcnU43McEvXztZfYz2IQqGEEJQc4YQAjMQ1GYxmxMinU7THePKBbVWaAQ/2K2JREKj0fA8H41GtVotyn3gpJjse01W5HJ5KBSCs50QEovFuGw2i/f1uR8V4ZnQR4Ghm4pZEgE4q5ilyD3NTq2rXl/51tHEhqcrl8k26V3bI1lQLBh4LwLSB867cDiMMkEAfqlUCjkeNRoN5YrTGFHaPHp8T9z+K5Rr1c+Tvc9kJd/9x7VtsQYgieFPnu8BbJ+yo04BEyXNE4ZyQ89OChoUCoXD4UDuneHhYb/fbzAY7Ha70WikBXew58LOLTDpL2lOep7JqEMFzwI0F7N8GMwPABS0MJPJqNXq4eHh3//+92fOnMExE4/HdTqdWq2ePXt2aWlpLBZj3xrzTxwb+cd2HxkLASFU86FXsk0VcsIYxGxMAnQeIEW8zs6dO59++mm/3+9yuaqrq7///e/X1NSEw+GCggI0lQaJ0gw57GBT14cgCIcOHQqFQiaT6Utf+hKOPfQSgmXZRpIsLGMBMfvXcUWpVEaj0dHRUb1ebzabz58/HwwG+/v7i4uL+/r6ysvLoZUtX758zZo1paWlTqdToVCo1WpolZS1ko+6EIvFNBoNHAs4t9rb2w8fPpxOp7Vard/vt9lsIJKpVCq5XK7X6xOJRDgclsvlQP+EkHQ6rdfrI5FIIpFAwABuheOZfX2SZwmx64Lk7IZk7EbAfjYYDMPDw0eOHHnttdfcbrfZbIbJf/ny5UVFRTU1NSUlJRqNBoAjHo9rNBoUyohGo08++eQ777yjUqn+9V//dd68eQaDgSISo9G4ZMmSv//7v//Rj340NDSk1Wp37NhRV1dnMplKSkpwNwwinRvs/BwXQeaiJY7jqC7BKoEcx8E7IcuWyiKEUNIRhcLkCmzP12qjlFCSJJ85RsG7wiOBHWiO4RlKbsIz6V/ZR7C+MtaZQAMzJEYTSq2kDeCz4Qe5rfoYoDco6ny2eHYmk4nFYljUQrZsPMdxCGHy+/3w005WJKcYzhSFQhEOhwOBQGlpaV9fXzKZrKurAyiX/PwqJqfP57NYLChsEo/H6bkGTUapVCYSCbr3JhKJfOmtr5WgnzG7kKsNG+/7rXh8VIQiEI7jQH+lyj91y9PijzisP+gmf6xEoVBEo1EgE41G4/P5wM4QBKG3tzcSiQSDwRUrVhgMhkAgoNFoJGhkYnDyCZR37Q32tCITKwAcwzBhgSzdKOkZKTDkePo9vvT7/Wq1OhaL+Xy+WCwWDAZnzJhhNBphWeSyJHuYQ+jCk1imBYZ9zg45fgJbEawp+JNMJguFQmaz2el0FhcXi6KoUqnOnj3rcrkikQhyscfjcZVKRSs0cRyHzVrMJk6B9iJh0F5h/7IXsxZlFv2jfxQKRTKZVKvVgUAAsMDn87322mujo6M4qERRtFgsGo0GFaCoL4KOCBnrW5AMR2tr6+joaH19fWFhISEkGAwaDAaTyQRFgmeq7aKFbPEpyUwY932TySQwYjwej0aj+/fv9/v9ZWVlsDnhOI9EIgsXLtRqtX19fZSpT7uCDjc7xPT+QANqtToSiWg0mmAwePz48a6urmg0ynHc6OhoNBrFPRFtQt07OGtjsRiOfPgQQDM7ffo0OPe0E2jXST7kCjeW30XVJD4PFzwYDCYSCY/HMzg4aDKZ4vG4Xq9ftmzZ8uXL7XY7VVeAGILBYDQaNRqNsVgsGo2+/PLLoVBIqVQ6HI5gMAj3FNQnGC9nzZp1ww037NixI5VKXbhwYWBgQK/X00XB9rNENZV8gLCvQP/EZgdiAS4CvgEsMILAFjTpUG6/jduf9IaSn0y80Nh1JNkiJNfQ15e89ZUf5LiexgjRH2KV0exb7P0la4d1CdLvqbKEb3IV4HEVgI+N0CVDS7mD3Ai7O7ZlgHJ4864asLI9L4oizoV4PF5aWopUEOfOnfvMZz7T2NgoifMmV6UAaLVaNJW6o6GNQ5PBMud5PpFIwLp5dS81qfYQQpBjA9/E43GkCcZ/P+H4id3T6E4CszRh1j4m59WxTD8Gwm5c17YHvF4v8JjH4ykoKEAqi56entbW1ldffRUFiwoLC+fPn59IJEwmEy3187ExhVxbmfichbDGsrwKAMlBfvgvtRDTuwhZZjx7GckekPB7FhQUXLx40WKxqFSqLVu2KBQKSv6h9wfpWa1WU843uEBkLHyRvADgNd27qeaAswTFobxe77Zt27xeL8dxRqNRqVR6PB6tVrtx48Y5c+aoVKpgMAgow7YfN6GGOsKAP5I9kFidBOcZJmiuMQn7PvsKlG0Fcxf1bR0+fPjSpUvpdNrv91dUVKxfv54QEggEZDKZx+PB0Uj7XGJNZMeO53nKBbr77ruRZVKv1yNVP+1PvCaLYFgcLOZEjkokGAwi9WQ6nR4cHLx8+XJ3d3dtbW04HF63bl1fXx84TjB4E6aWKi0JzHEcpgGr2NCnwyuCSFlRFH0+36lTp1BGl+d55NgxmUwwqiF5DtAVcmtCM8TFo6Oj586dO3v27M6dOx988MEVK1ao1WrUQ5Cc92J+uzV7JXs9GzvB9qRWq8XsEkXRbrcLgjB37lxUQwN/CRV8ESaRyWT0er1Coejq6jp+/LjH4zEajXV1dX6/32Qy0YyWGo0mGo36/X5CyIYNGwRBeOGFF8xmczAYpA6EXMTJalYSoEyhbe4coMqkREkDuIHLTmTitiVdJFHncmWyruTcu4lZvw27PPNdRq4M1UkwvWRl5d5WMh9IVmGg14g5yXxZtwDJCe6n40UXIPund23/h1+wYCORCHZpwpDrQNiDJqnVakVRRG7cSd0/VyXD/eFJEAShtbX1hz/8YV9fXzwedzgc9fX1KCpPxu72V/FeqPqn0+lg3EH0mkKhSCQSoVDI4XAQQuDOxWY+2UdMSkRRhAaiVCrByIU9BZQzMpbK8gkUdoNiMyazRkAuS+djcc4nRHKXwLXtAZvNNjg4WFpaqtfre3p69Hp9V1fXj3/8YxTHlMlkLS0tTz/9NMdxM2bMkJxB17AZHxt51y2LHlXixB4Adl+QnEns7+mfeCYtHbZRLBitVptIJORy+eDgYH9//8yZMxsbG10uF6zaYIIizwnYz8B59FYsRY+MR3Kl2FfI5rLEb0G8ttvtcCQ9+eSTwEYOh0On01ksloKCgg0bNiAc1uFw+P1+xJVS3z2oKXgKO9voi0tQFNvOXGFVBfYbMRtGjFSPgiDs3LkT/uKampp169bdeeedZrMZFBfEOZBsPWPCmBIl7eSyNVlvu+22/v7+uXPnov4UUGYikchlEktayI7vBDoASgog1X0gEFi6dCkA+qZNm2666aaf//znFy5cWLduHSEEVFSDwUDBkJCtboY0+bldTbKKUygUstvtAPdDQ0PoN5VKtXLlSkpzR+dYrVYMHOI3dDpdJBLp7u4mhLz++uvvvPNOOp3u7OwcHh4eGRkpKSlhH8ozmaPGfVnCGMInXmn0r3K53GQy3XDDDWVlZciCarVa7XY7YAEbYk4IUalU8Xj80qVLu3btev311+12O8/zt99+u81mg309GAzCEwr9AabTpUuX/ulPf8Lr08lA62ZIgDiNkqdAWWSyXREG91CVgH1fqqFBpaHh77gMMTASSEHn+bgdlW9eXQX2Glfo/iAB0Fd4foy7xtnv6XqfoMGSPZeMVTA4xlzCUox4JtaffRblQ15pF3wohfL+YYrG5k+Df7AZ4kvx3Vhk+UQcq8JBcC4kk8lf/vKX7e3tIyMjDQ0N0WgUgI/O9qtGGIjmgoUCuj3HcalUaseOHYFAYGBg4Jvf/CZhTMvv9ziGQiGj0Qj1A+xqQRBQPBEXfNQn0nsXaumjoB/nEc1IwYalvd8K2ydQAMxMJlNhYWFra+t//dd/nTt3DnqXUqk0Go1dXV179+6tra0lDCuEyrU6KT4ekm9+5jtnJ1IA2E2Qboss65cwB5XIME/wQ0rvBgEjGAwODQ1t2LCB8jthmQD6BxDExsRlqe0shmCzD7GGNGoW5bJGNXwAho5Go5lMprW1FctbpVLNmTPH4/EEAgGbzWY0GiORCCFEp9PBIcu+I9pPT30KYlggxfZ7Liik6JleT39Cu5Hn+WQyCWZIKBQaHh5ubW2VyWSRSGTTpk1LlixBsWQg2mg0arFY6OFBGPv9uJiA5/kVK1ZEIpFQKGQwGEKhECEE2eipAV5iu5WcfO96PIBYL4oiNL3GxsaFCxdWV1fX1tbabLaKiorCwsJ7773XZrOBfA/qF9yptJGSbiQMWoIOGQqF4CHBKPh8vnQ6bbfbFy9ebLPZQPtBHK3T6QT7CIl0fD7f0NDQ5cuXm5ubT548OTg46HA4ysvL6+vr9Xo9XAS5bzoB5mChpJjH3szOBMSQmc3miooKAHeHwyFmoz7ojKIp/M1mc2VlZXd3t8/nCwQCN954Y1VVVTKZxAgqlUp48EFXUygUoVBIJpOhjAD+hZMHgbnjNiz37XL3UJGJaZHMDaqWo8+5bBg3lhjJAcoTTyEJ9eVdd3P25uxPWM59vrem/70S0DMu3M+9eb4Nl/pG6K8o3502idJ7WPUsn5Yy2fZ/yAVbOoB+JBKRyWSg+oii2N/fr1ari4uL8ZqAAu+ds44+DwaDoii2tLQcPXqU4ziLxZJIJGpra8WcrNMTK675BMxSQgjC3tRqtdPpPHXq1B/+8IdQKBSPx++55x6LxYL66Ij5eY/vNbFQIhk8wNgqqXUA13Bj/V2fKGG3RBr7SwhBARZCSDKZRMwYn5PY95Mg77oBvkcJBALIGxEIBPr7+8+ePXvixAmz2SyTyUAUJ4TEYjGn04kBohRoMtZ3PVlP8sdVruQAZbF9XgVAyCbC45hqtXSXlND0+WxBK4HJXEHXFeI8iouLb7vttvXr14uiWFJSEggEgPu5bNwtEryAjk9/Tv9LmGOS7stQG5DjH2o6yRrFQVDu6+s7ePDgzp071Wr1yMhIXV3d9OnTDxw4kE6nFy1ahNyjqLEKX7OYDV8mOROd1UZoX4+Lufmc1IcUL+beChMaO3J7e/uuXbuQKaK8vLypqclut+OeIHWAD4PNCAAaFGTKL2KBBZqB+LlIJGIwGAoKCkKhEOIKqMIjWeHsf1mLeL6JpdfrUdcZtu2ysrJvfetbdrsdkbubNm2qqam57rrrUqlUIBAQsnkq6VCKWdWFOt9ZrIYX5Hkejpp0On3u3DmUvNVqtQicQG+A7ySKYmFhocfjGR4eHh0dbW1tPXv2bGdn58jIiE6nC4VCCoUCdQPgAkLeJ46xeVM8ly84b1y2FTvc7CuIoujz+Ww2m1KprKurwzYXDoczmQwtgE17OJVKJRKJ1tbWRCJx6NChVCql0Wjuvvvuuro6hArwPK/X61FZWchW+fV6vYcOHUK2qKKiItCl6IbITkVKkpFMaZFB6lToxRKAzjEaOPXh0DtQ/CoZxAkk3xqZWNghwzf8eAEMJM9xdeVn2LjrIt83uT9kTRXsD9l1R0eBLjrC9AbH8OLoTz4GWIQm/0mn09FoFDzgCxcuuN3ul19+OZPJ3H777StWrNDpdIIguFwuZA+7chHH+rgIQ8Q6e/bsn//8Z6/Xm06nS0tLkZiOYzRb8T0oAFBjwGJCepODBw++8sorly5dSqVSS5YsQSlDZJmbLK/pKgQeOZrvKBAIpFIpm83GUh8/yYLTEwco8GU0GkXwmN1uR2IJSpeCJvBBN/kDEDGPqeu9i8lk8vv9ZrO5paXl4YcfDoVCFRUVHMfV1tZ+7nOfGxoaevjhh8GF1uv14XCYZSyLjFzbVn105UoGiD0r30UBkNyO7qSUpk9PL2qn4bP5+yE0P6PVav3yl79cWlo6ODioVCqRTgtIPRaLIS067JeU1EHdsjyTBYgw8BSrF3iIsr1xmUql8ng8KpXqscceA++lvr5+/fr1zz//fDweX7Bgwd133x0MBmOxmFKpRJo5uuNT0yzbWZIFAOBIj2eqosDiy40NFcX3knHis2kWsTXH4/HTp0+/9NJLCoXCYDDccMMNpaWlhBBEvsJCBlcA0kqwuTKFbD5+Sq2hgE+n06VSqcLCwu7u7uLiYoVCkUqlEI1KB1RgMi+xsQr0BXmel7SfCtI6IWgVwzRjxoxwOKzT6To7O61W69atWwOBADzv5eXl1PiUyVYWw85LixOzJlU8HRqR3++PRCJ79+51u91IpTc8PPzGG28sWrSoqKgIPw8Ggx0dHQcPHjx//jxCSiKRCFIDIRmoQqGw2WzV1dXogXg8TruRXBlslVyQD/3T/zocDp7nR0dH0+m0w+FAFDI10ouiGI/HOY5TKpUommaz2f7yl794vV6lUnnnnXfi1cCThvESmRDQgfF4fHh4ePv27YFAYOHChVarVaFQBIPBgoICeFroxi1maVfjHmAU8bBTl8VAdCZTqzbbb0I2jJ7tQxbZSxDwuP0p+dVkTxqJosIOUO7FVwHsaCdIhjtfO9HbGaaOOFUJaBdRxZXneeTEzPWGSQwKImOonlT7P4RCi3sgi0BnZ+ejjz567ty5S5cuNTU16fV6QRCQ/s9ut1+TrDWiKI6MjPziF784duyYUqmcMWNGOp2ORCI9PT3Tp0+ns12im01K5HI5gqxMJlM0Gj1y5Mgbb7xx5MiR4uJiuVx+xx132O12mEKi0ajZbH6/s/Eg0AgZEfbt2/f888+r1eovfvGLdXV17Ez+xGoCNEqb5hEJBAI+n+/s2bOzZ89GAm4umzj1Y7DoPmwSDAbNZnNXV9ef//znTCbjcrlKSkp+8pOfIEnj8ePHE4nEhg0b5s6dOzIyAv6w5ITF0fbJVMxy5V0XMsWx+CBndwGqXZFskBZ7VuH3VBuGugxiBtAnOPQmkwmKGlBLOBy2WCyxWGzOnDlKpRKOyEAgkMlkrFYrx3GAaHa7PZVKwTQCaAheAZtUBJmCQBbHchUEIR6P4/5AroIgQLVwOp16vf7ChQuAy6lUauXKlZFIJJlMGgyGZcuWgS9Osuicah0iw0HCo1noT6cdDLHUP4in0Neh+wWsC7g+Ho+D54qc0DAziKIIJZjjOKfTiajWqqqqtWvXIl81iO80CXGaKQRGxi4AijawZ2G8QKHx+XwqlSoUCun1euQy4zgOERfINYQYA/jix2U1UCDCal88z6vVavQ5zZgElSYSiZSWlvI8j1x4giAUFhay1dzovEJ3ZcZWYqbXIOAVCkY6nW5paaHnNEyD27ZtW716tUKhGBkZOXz4cG9v79DQkEajQe4pk8mUSCSmT59OCEGeJa/Xu27dOkSHw8DDLgzWhj3uuqLeIfoNfRH2h/TnULTAbUMeJ5p7lCYah4cHjIhwOLxz506LxRIKhcrLy00mExILYiihOYDqg3P9mWeeUSgUsVhs+vTp0KUxInQyUJcd2oy3xkpBJT6v12u1WqG40kh0ZCbNZIUuCtwKMYWSvpoYNuXrT8k3kl698vvku15g4sbY7yXZtMi7qX/jPmhiTICVSMbSfnJd1ehhNtsS/bmkzayGkG+jz+QpEMYuW/b6yWKaa4UUBUEIBAI6nS6TyahUKgzTkSNH4PgqLi7GEsbcvoocoKlUCvkksMNg7zUajW63+9y5cwUFBTzPNzY29vf38zwfCoVQB0bMRvOjPdiuJ/tehBCZTAa7ZjAY7OzsLCoq8vl8s2bNqq+vJ1nVzmw2w4o0qftnspVeYPYi2WmGs5gmkkZWJblcjt2D5/mOjo4333yzpaVFJpPV1dXV1dVN6rkfV+F5HkcMXCWCIBw7duzw4cMnT578/Oc/X1ZWZjQauWzkIZ+fZzjB/emBRREOzQaL0plgwIPYOdmsX/nW72TnVb7rr9V6x7oGeozH42Bf63S6QCDQ1dXV2toaCASQEOy2225DephAIDA6OooYoZqaGo7jkO9bsgPT7r0m7X9XQ9V7lPf7/hIcwsJ9cWwiCsxMOcdYqdnfsAoW23QKLoVsYCj2I1EUkYcxGo1iJwJe1+l04XA4lUrpdDrgXYPBAPoyoDwFGbBqI5YRS441/AOg8Aw1H++DxC9wMsBWiqjigoKCc+fOvfrqq4FAAPkWBwYG2tvbS0pK7rjjjgULFgB0JpNJk8kkji0VJJlP4tgan+JYZj8OdYnpnSVgoMdisZhKpQKhH10kk8lgyQZi9vl8J0+edDqdFRUV3/rWt8xmM1Qgqo2AA0NbSIcW7WEJ5exgo2O1Wi1AZDwep3ly4BihE4KSi/JNLMnrcwxRhGJNSafR6T4uQ0NkjKNs1hTCLOxIJIKhNxgMra2t4XAYeTYQZufxeLZt27Z9+3Yc1ZhmeK+6urqRkZHGxkaknX300UeRhaOiouKee+5JJpMILcD+zuUUbxbyBHdKYj/oAqEbt2SeEAZ1CdnyT4QQ6HLQneAlg3IbiURQUctkMq1evRoJBGUyWSAQKCwsRAwfIgFGRkaSyWR3d7ff729sbIRlsaioCCoH2NVkbHAOvqFLiU4hQRCAtIAkwK3CVgLPnmTK8UwBLMkrUyD7IRcW97Nfvn+Pe9ctPnfm5G6/ZJL4Q9KGfE/8QKSsrIwQglNg+/bt+/fvx3bkcDi2bt0KbyFMDEAGk7o51GCSPV9QY6i9vf3pp59WKpXhcHju3LkrV67cs2dPMpksKSnhsq5OycE52ZdCDADOsmAwePToUZfLFQwGFy1atG7dOuSkBu8fOvxk1wv0JeRctlqtb7zxBlLDNTQ0zJo1S5YtnU6RFso+Dg8PP/74421tbV6vVyaTzZw5k51O79+0//BLOBwG3MQm/LOf/ez06dN9fX0333xzZWWlTqeDFZLduiclNFcHx5gbqMKfSCQuXbrU39/v8XiWL19eXFwMw+vHT7RaLdYgwh0JITjZ1Wr1r371q4sXL545c+bOO+8sKSn5zGc+AxS3e/fugYEBQRCamppkMhlyl9M04qx8kifwFQpryybMeSTPPQNyzyHCbIisRYTLGk0B0KEGwG4timIoFBodHbXZbIjOBAkbn/V6PbAdx3EI1qS2MYmFlcuWm6WQmoytXAsbKgzYlA8DreC11147deqUIAiJRMJoNF68eDEQCKxatWrz5s2iKMKgjjzrrGGMhQXssU2/p4BPZCqFwSwKmy77Je0rWt6C4zhkx4fYbDYw0V944YWOjo6ysrKFCxfinIBRR5atqQzaFccI21FsQCE+4CRAe1KpFBjwoJLjLUA7oW2DbTifBUKCliTQhG0S7bpccyPHcaj5xV4pZvMg0aew3gaUSQ4Gg6FQaPfu3S6Xy2azGQwGjUYzMjKC7RL8KPiUZDLZ3XffPX369MbGRr/fbzQaPR7PN7/5TeTXLyoquueeeyoqKhKJhN/vpxF4kvaLOUwP9r1YBYZK7vJhf8J6hNi/YmYiFN5gMPT19e3cudPn8/E8X1tbW15eTucnfEHIKjgwMIBv/vKXv0QiEaVSee+995aVlcGqRHOq5mr8JFstgcsapwkhmCHwQfHZBCxg4hFCMF6sDZv+lt1QPlgoeRXC51DtqR54Te7PjbWnkLGbSa6ws2jcHfjKn5v70HEPyL/OkOU7m8HrQ2qa8+fPP/nkk0ePHkVpMIfDsWDBAiTOV6vVQGCTfS7sOzTMRqfT8Tx/6dKlt956S6PRlJSUfPGLXywtLYVHcc6cOXxOoMUEjZ9AAFBSqZTf7+/t7T1//rzVajUYDFVVVStWrEDCE5Q9ujptmZ4jBoOhq6vrhz/8YTAYLCwsXLx48de+9rXS0lKaMpXm45LJZL29vc3NzRzHGY3GysrKFStWTPa5H1eBN57neWSzOHLkCHDqmjVrqquraRlEHPdXl7aVbptclsyMUyASifz3f//3nj17hoaGpk+fvmTJkquoeP1RERx2YMDiDDIajT6f75VXXjl06FAgEJg1axYSZBFCksmk3+/fuXPn6OhoTU3N3LlzkSYboSzs2qT/XsW4fKJEyCaJYQ8+QRD+Bwuy1mv6jQTcYAazpm56F7gdaT4fnuf7+/v37Nnz7LPPDgwMKJVKMC44jkun07FYDOkCcU+lUgmII4oiqkqBXSCBWfQbCkcy2fTwMLdQEjkuCAaDp0+fNplMJpMJKXRQFGzp0qWwp8L7BrIKhWgcEwrGMfx+ejzQz8LY8D56bFBTAWtkhaqAd0cuTkxoURSDwSC4Unv37oUmM3v27FAohF6FUiEylZXHPZNYVEoYMMFxHLjgiCHW6/WotQGliO1bel5yeYQwiCEX3Eu+kWALCbiRIGmRqTRHcjSNYDCIc8vn8/X09CBvbEVFxa233jpz5kxUBoGzGxNs4cKFc+fOLSsrGx0dNZvNoAH4fD632+12u1evXn399dcPDQ3BUQC1gaZ7Yyc2He7caZD7auNqArnvyK4aSrkh2UQTyWTy3LlzO3bskMvlixYtuuGGG8CRwK2SySTsH8XFxVqtFjyuHTt2RKPRkpKSpUuXgs8TCoUw6EgWBKczS69iPTmwFMJzgnSluMztdqfTaaQhAhEI7cdsFHISXn2Y0X/uANHxlVxwdfefYL1wDAqXXDxuCwmzOtjxohYQ9sMVNoz9b+41H+DAwf+GfXv//v3d3d3wDCP/AUiJcDSl02nqzrpyobFM2Kg1Gs2RI0cOHTpkNBrtdvv06dOXL1/u9/vhCcQRwI0NK7y6zoEVDGmRBwcHo9EoAoLnzJlTVlZGuXk0A9JkRRCE/v5+pLg4efJkIpFAypRMJlNYWIi0ibRgbSQSQTTRhQsXCCFKpbKwsHDOnDlOp5OMnY3jTo9PgmB7JISoVKoLFy4Aomi12oaGBqvVChscxyQdmez9OY4Dx1Vk+MCiKIbDYa/Xe+HCBa/XGw6HFy1aJAiC1+u9ivvn238+VJJMJpF1Aycaip86nc6//OUv4ObdeuutVqvVaDQCTB4/fnxgYCAUCq1ZswasaYBDdoekU/dD+L4fNmFRDbvXySVUB5EBeeyPJZ8l34hZGgwIA5cvX3777bf37NkzMDBQX19fWlqaTqedTifQmNfrxWV4BDyhJEuQoGiMHWYuG5uLbV0URcA+QRBABRGzMZHQQARBOHLkyMDAAAge4JiuXbu2sbFxzpw5fr9fLpcjT6LVakWZAsKAePaleIYlzL4vOxFJdp3L5XJUdYVqweI/+j0+C4JASTherxdpH8vLy4uLi+vq6vBcxDkQQihRR2RgtMCk7ySEIA8jS+mhbhMU1IQzAQV04WKD+RxHLHSkCYpTcoxtjPrKqbZDlyILsGj/CAwPG8qexKLMMRQg2rGUpQ0T4MGDBy9fviyXy1Uq1apVqz796U8vWbKEEELz/QuCUFlZOTIyUltbC0eHQqG4dOnS73//e2CI6dOn33jjjVqtFl6FgoICj8dDyy+IYzlL+YKeJZVx2anC9hX9TCcnndJ8NogTrnwgdVEUBwYGDhw4gEqlt99++7x587hsun0MIgIDwK9rbm6+ePFiMBgsKipau3YtpfySbAlSEL1YuyadQsi4hbMNO3I4HJbJZOfPn08kEhqNpra2lud50DSFbK06yazL1z/jumg/hMKN1TPJ+4OGWejP3p9dHe/HQyW6B1253Fi155o/+soFyozT6RwaGtq7d28qlbJYLD6fr6SkZPPmzfA7wR4Bl/Jk7w9nLLY1QRBCoRA4MCMjI3ffffeGDRv0ev3OnTuREgc1CiUbO8dxwuSDC8HwBN0IpCar1drY2LhmzRqYP8FjRhQN3YSvXHieh9Xj0KFDf/rTnziO83g8Go0GjkGz2azVao1GI9CS0WiUyWQ9PT27d+92Op0mk+nuu+9es2ZNYWHhFGyCIFMTz/PJZPLxxx9Hvo2bb765sLAQpzPJ5uy+OhszphBUWcpz5jjOYrHs3r37woULHo9n5syZ69atKykpgTf4Wr/ih0LYAwhR8m+88ca+ffsIIbW1tbNmzbrxxhv7+vp0Op3RaAQRS6fTzZgxY/Xq1RTdIaPJB/wmHy+RRp5RYY8o9vTimQx0LIwDOwVYbf/+/Xv27Glra8Pqoslc9Xo9mAyhUGhwcLCqqkqj0SDwFLhBo9HAXkKyQFPMRh+y6JZkk2yIophIJGgNqUw2Dtjn8z377LOZTGZ4eBiVoTKZzNatWysrKw0GQzweB+4Ph8MGgwE3pyiHzyYgom2Q9AzeV2Dy7tNG8jxP3RfUhA8IiDBQbP1U1REEQa/XZzIZZCbVaDTr168vLy+nbB/26bg/FgDLmWGVfgqp6SBqtVqPxwMTUTgcPnnyZDAYzGQyqEtlMplAf4R6wOVnKdD741n82LxMklk0Ac5gEQmrGPBMrVNWZ8CpOTw8/Nprr3V1del0utmzZ1933XWiKCJCC5mtUesgkUiAWNzZ2VlRUQFv+PHjx3U6ncfj+epXv1paWmq1WnmeRyyBSqVCyDjtN6ryAUCzPUyymEBycNI/Sd6dZCMrREa/pRdj+BD1BefVyMjIwMCASqXSarWzZs0ymUwGgyEajcLsgThd+M3a2tqeeOKJvr6+WCx2++2333zzzclk0mazoQgRWK1qtVoyK7BkuGz4hyiKmPkKhcLn87399tuvvvqqwWCw2WwPPfRQJBKprq7OHXfWByV503GnzQcuE8NcSbPFydN8813P5aEA5VM8+LEpm+gHISeIWTLfrqQ9+dDeVbzvZCVf/yeTSb1ev3379uPHj3d0dPA8j1yfpaWlq1evxnEgZCNk3svTQXQEyTgYDM6YMWPp0qVWq/Xy5cvhcBiZ1ijqyt3bJysYr0gksm/fviNHjshkslWrVuHoQaQpzQFArmrVBINBtVrd39/f2trq8/mA+L/2ta/NmjVrxowZhOlwFFIdGBh4/fXXXS7X9OnTp02b9rd/+7dIj0Zdix/alfvXEdiJ0CfHjx+XyWRlZWV333032GiUnMxnw/wme3/KiRCZDOOpVKq3t/exxx5rbW0tLi7+whe+MGPGDIR+TTbW5aMyfDKZzOVyFRUVgQDCcdyf/vSn8+fP19TUbN68eeXKlQ6HI51Oh0Khvr6+V155JRwOr1q1at26deXl5TKZDHGSEqaABJpOyQRC7YD4L935xxCq2A5lD6rcn4lZ+juFONB0Y7FYJBJpbm4eGhqSy+VIuZhIJBQKhdVqhdHd6/Xu27dvdHT0rrvuqq6uhiMYhklK2eTHMjJFhnZPURrWEioosWqJz+dra2sbHR21Wq2INM9kMjNmzEBGRdReicfjeFAwGES+OZbWzMJWqgmwWAofJDwl7A5wbojZAuwU/4GjAscF+5T+/v7e3l6fz4dUlXATWyyWYDBIGGoNlyVfUb82VVeogYHdniioRe5IQkhvb28ymQTjcHh4ePbs2fCDV1dX47Y0jc+4gh1QZETIRhhLJkk+mxlGTQKIydjUCrmzFl5sp9Pp9/tRGXflypU8zyOHRigUolVaCgsLoTjJZDKkFff5fJ2dncFgUKlUmkymqqoqkAiHh4fNZjOikVhlDI2HkLHaDlUDaD0ySTvZnmfXFP0htSHRi3FCgwsUCoXa29sjkUg6nV68eDH0ZIPBQDMFCYKA0OehoaHTp093dXXFYrGioqI1a9aA5JZIJKA8w58DhZMKOwpi1s8Gv0EsFjt79mxbW9vAwABq9l28eBE2UeqswBKj/6XrJVf5+ahsx3SXYzcZMZsE5prIuDrABBdP0M7JPpfk39L/ypKv/Tgsmpub29vbQYmMRqPXX3/99OnTYbempr6r4F3gV9h+EWmwf/9+pHueOXNmSUmJ2Wz2er2oFlJdXQ3qPM+ElpGrpRaIooiiJUNDQ8jyOXv2bIPBgGqPlMMAzZ9GKl+54A49PT2XL19G1FZtbe2CBQuwY+DmKEUMtBQOh7u7u0dHR6uqqqxWKwL9EaQ32Vf7WEokElGpVMPDwx0dHWazGZyCSCRCdzxKwb26zY3un4QQqH8g/4yOjiYSCavVWlFRUVdXhxRA70XX/ZCLQqFA8Tssq6NHj46MjOj1+iVLlixevNhoNI6MjGB+7t27F578G264YdmyZTCqitnSmZKwGQn2mJJ8QhEyC0J4npeznGCJdUryX/qBRguwZiqw/IPBoN/v7+zshA83lUo1NjbC2moymbxebzAYPHny5KOPPqrRaFatWlVVVQVcG4/HwcsHDEVjgOeoHxYJgmBipzG4CCODURPwGhWgOI5zu90w8NfW1n7rW9+y2WxAUXq9vr+/3263G41GpIwEaqdqOu0vPpt1VILwCOMcoJAI/8ZiMbY/QftRKBTDw8MGgwERz263Gy7vgoKCRCLx6quvwvawYsWKkpISn88Hkjd9fTEb2YzsRiITNStjaibQkaaDApJ3Mpk8f/78T3/6U6TSLywsdLlcLS0tTU1NJSUllZWVlJMDe/O4E0iiAgFBstnfaZ/w2foM+JPEzM/2ITVIy5g0oCxY4ThOpVIhiwUSBouiuGLFCrVa7Xa7LRaLUqkE0sVZiGRQXq/XbrcPDAxcvHjxv/7rvwRBmD9//m233WY0GsvKymD9AqfQ5XKhn8GVB/7DNi0wtaVZJphkdbAaIP2eoi56DXUK0ReE4wJAJxaL9ff3t7S0IG5h69atVqs1GAzSPInpdDoYDFqt1j179vz617+mFQPuueeeWbNmud3uyspKlFT0eDxIBOR2u7NBDf/TybQ6B6AVqK6xWCwcDjc3NyNG0Ov1plKpl19++TOf+YzT6YxGo3a7nTabTi2sOMLgSxZuvstW9OEQqt3R/0o0pWsik+oNSR+yLcxVISbb1NyW/HVOzXxPicVi58+fP3nyJJeNp7JarQsXLrzuuuvAy4dnjGTpwpN9Ls/zSPqsUqn8fv8777yDfIuzZ88GEzWRSIyOjjocjg0bNlD0T7V0Pof5eYWC5TA0NHTu3DmbzZZKpZYuXYrCW8hqStlNwWDQaDROVgHgOC4ajY6MjHR1dRkMhjvuuAN0JpBdcQoTQuRyud/vj8fjP/vZz3p7ezmO27x58w033CAIAlb9X6EG2UdCwNR95513XnnlFUEQampqVq1aVVtbG4lE+GzArsT8Man7w5eF0xB7ZiQScTqd//qv/yqK4po1a9avX19WViYy/oGPpWC2I3Od3+//zW9+E4/HbTbb5z73ObVajb92dnZ6vd7nn38+HA43NjauXLkSnKhIJIKqwJIjmMpH6Nz5oISCMdp7mM9yNvsKuYKDEEgLwIUaaQRB6O3tRd73F154geM42FzvvfdeoDGe571er9fr5Xn+X/7lX2AasVgsXq8X1E9BEOx2O6q0otSrTCYLhUJqtRrU53A4jI0bNlFktQ+FQm63e+/evZcvX54zZ87cuXPr6+vPnDlz/PhxxES63e4777xz8+bNDodjaGho2rRpfX19Wq122rRpo6OjgiAYDAafz4cqBB6PR6vVGgyG9vb2mpoa1NAF7gyHwyaTKRgMGgwG/AvjCqIn5XK51+u12WwdHR0XLlwIh8M+n+++++5DdAvmNxqsVqufeuqpgwcPjo6OfuMb31i6dKnb7W5ubuZ5XqfTUXYmUtagUIBarUYpXyQSjUQihBCNRgM+nCAIiUQiGo1aLBYwuTOZjM1mGxoaSiaTVqvV6XT29fX97Gc/Q1gnlGyNRmM2m9va2mhCerpDwUYO4bNEfyFb6YkyvIVslSKO4f0TpqYYa+AH/AWND3fgmJpTmWxROboJsv6c0dHRTCbzxBNPAKlXVlaiUBowMeyIsCDCF6TVarVabXt7e1lZ2euvv97X16fX6z/1qU/Nnz8fOX8A9BF3a7PZ3n777SeffDIajUaj0dtvv91ms6lUqurqatiBRkdH6+rqKisrkWyHjGePxNph+WMsdIDxnl1lYjY+D9HYmB7PPfdcW1ubUqncuHEj5iT8ZhzHuVwui8Xi9/tffPHFl19+WRTF/v7+xsbGz33uc4sWLULqT3QCjPqIEfR4PA888IDb7S4sLIxEIiUlJfPnz6+srBwdHTUYDLNmzQK/SCaTRSKRXbt2cRyHdZdKpU6cOBGNRv/hH/6hoKAA1+v1+kAgYLFY8AhRFFUqVTgcRrhhPB7XarXQxqm7HNMJrjakLsH81Gg0Pp/PYDAgmAGdo1QqEZljNBqRhWOyXM9MJqNSqUAIhGMnkUgAR1IrEaYxhkmpVLrdbsAmuL/ATAUlDAQweCyp619iWaczlur/7MRmgR37mVL7WEVdZKg+40quFSbfgTeuhjDuqXl1lvXJCjhmUDuxpaRSKVQ9+/Of/4wlVlBQoFQqp02btn79eswoGiOLnCGU/wliGxIY4Axiw8kIc37J5fJQKFRaWnrp0qW9e/e6XK5oNKrT6YC3BgYGnn/+eeycjY2NcAvTrPzcFSQVydf/mMxo8OnTpx9++GHYGkApIYTgr3gvWJdoNja0nJoGYJwm2S2UEBIMBtE/58+fLy4ujkQiW7Zs4TjO4XDgnohny2QyWK1nz549cuSI2WzGi9tsNige2D8n9V6TFW6s95527AdorI1EIjBCQxkjhKC7RFF85plnADkWL168detW9DOWKq0PwF9V+W04SxHuwnEcNpx//Md/BGf1C1/4QkNDA0K2kPAq38T7qABchLXw2ZKsNCzNaDQSQhD3vG3bNqfTuXDhwnvvvddqtep0Oq/XG41Gn3zyybNnz6rV6vvuu2/9+vXYfrH8EceYbwiuTlcfV67V/Pygxisf0KdHmGSrHIcCRP887gP4nOhPXOlwOEKhkFwuP3jwIHIXrlixoqGhQaPR9PX1TZs2Dfjv+PHjqIqCDLuAhghDzGQyZrMZMCIajQ4NDVmt1mg0CqMFPANGozEajXo8HofDkUgkXnnllfb29o6OjnA4PDIywnGc3W7fvXs37PqBQGD69OlNTU2EEJCkPR4Px3FOp7Orq6umpgY1sOAOjsfjBQUFgiDQwACO42Av93g8wWCwt7fXZrNhDUejUWBNmNhjsVgikThx4sRrr7127NgxjUZTWloKTYmyMgRB0Ov1Xq/34sWL4XAYntlp06a1tLSo1epoNApHGLhumUymv79fp9MBdEYiEVizaBgALNbwNSN5fDAYLCkpAa6FyQcG8nPnzj3++ONOpzMSiVit1oKCgmAwWFFRMTIyIopiJBIBUR5rFdiOusIpNIETBsPNM5UmIByTHJaqARNMHsLgJ/Ym9IespVwmkx0/fryrq8tqtQ4NDTU1Nel0Oo6JjhWySah4npfL5YFAQKVS6XQ6p9M5Ojoqk8nq6urKysqwy2ATQf0vuVze29u7a9cur9fr8/lSqdSrr74KxdVkMuEMViqVa9asQcogND6fh4Q6jiQrn/UksMsPRxGwZjwev3z5stPpXLVq1YwZM2CUMhgMWAsymezy5cvbt2/v7OzExK6url6/fv306dN1Ol1BQQFWFmA3z/MASS+//DJMJiChDg8PO51OtF8UxYqKCrvdDk0jGo2KouhyuUwmU0VFBZKNjoyMuFwuoLe+vj6LxYITESZVQgi4BIQQFFrGlEYiF2roUiqVCMQE0Y4mXZVlM9sC68MYCQ4DLkCF5nH7OZ9kMhn8HAmvoAmgLCvP0OQQ7oI5A86YKIqBQECtVmNEXC4X8pJxTO0R6NXjPpemSZVlC0hlsnUMx70+H0Cf1Mt+tESWrbiCQpCEkGQy2d7evm/fPmi5BoOhuLh42bJlXLYkNhQwKAzoWEwkeujAbShRolismUwmQWPr6+vDGtfpdNdddx1sKMFgELGzJpOJ53l4EdHa9zgWaJJGo/F4PIFAAOVcuGzpDPoKrNOV7p9gm1AbikajQcAAggeQ4ScWiw0NDWHLKi0t9fl8DQ0N0F1pthmlUgmGZGtrq0ajicVia9asMZvNGo2GJn54L+/4URRkj0APYyAIIUiGMTAwAAtFIBCorKwkTBGV9y5i1hUPI6NMJuvs7NRoNDDioBZqLBYDSLiKoPAPm2i1WjHLfybZmYbl6Xa7y8rKfvKTn8C7Ul1dDYvS8PBwQUHBhQsXWltbU6kUDHYwRNJDk/rhJ+sx+6TJuPvYBFqNtA7Au3oAqNWWIhvKooYZTxRFJBFftmwZEhEkk0mv15tIJH7961/39vYGAgFCSFFREawRgUDAZrMhVNfr9XIct3PnzhdffLGrq+sf/uEfli1bFggEKioqsIBHRkaAKn7xi1+89NJLRUVFg4ODABNms9lkMp0/fx75pNPp9MyZMzdu3NjQ0DA6OooQ21AoVFlZ6XQ6Gxsbg8Fgf39/f3//L3/5y5kzZ37pS1+qqqqC7gGLOwhFfr9/x44dyLf4+OOPw/BssVhGR0cxO8Ph8J/+9CfgLVDPR0ZGUqlUf39/WVkZn60FRgjR6XTd3d3U6+3xeFKp1JNPPunxeERRXL58udlsBoiJx+NmszmTyfj9fkIIzeyWTCYDgQC6t7u7OxQKHT58uK+vDx6D73znOwaDQavVptNpo9EYCAQOHjz4gx/8wG634xyqqqq66667li1b9vzzz7/88ssGg4FiOOrPoa7hXCxOGEOOROfmGXY4pZ3kzpxxFQD8FYCeKgN8NuiK5/lt27bxPB8IBDQaTVVVVUFBQSQSgUMA12SyCUxpPWaDwfDiiy+63W6ZTLZmzZrS0lKXy0XjuuDG4TguEAi89NJLyKlnt9s9Hk8mkwkGg8iHAweR3W5vaGioqKiQyWRsJWOJSGz/9AVxtOcyCrDpa7XaZDIZDocjkYgoiitWrFi+fLlWqz137tzg4OCJEycuXLigUqna29tRvUgmk9XU1Fx//fU33HADIFE4HAbGRb0kOKwjkcj+/fsFQUDWIFi4DQaDUqkcHR3V6XQDAwMnTpyAuR23BWUCR2NBQUF/f/+jjz566dIl9NKaNWvuuOOOJUuWAGGgD4eHhzUajcFg8Hq9Wq3W4XCgWjOXrSzG8zwUWph/wLtAgDJiDwC+0Tyw4wghAN/5+jmfQFXGfIb+AMKYUqmMRqMul6u3t7ejo6Ojo6Ovr8/n82k0mmnTppnN5nvvvbekpARrTaFQoEYsyWpuHMdlMhkais2NjaMi2SooVPHj8we0sAtBHMvz+QDNou+30G1EyOYAQQWPxx9/3Gw2DwwMaDQav9//6KOPOhwOmUyGZQ5EThNLEMaBw2dp2dgPoVWyGxRdaPF4vLe397nnnkN9lUWLFt15551arRaph7q7u202W0NDAxJJ5556VzcoKEZpNBpra2sLCgqKi4u5bH4IaoSjUUYwacF4L2b5h1BKYYDA62O9AEsB0J85cyaRSDz00EPIecAm/kdOG0JIb2/vq6++irtt2rQJBFRceRUK9kddcJSgV2niB4VC0dPT88Ybb3R1dZnN5tra2jVr1sBPMsFWPymBcQ0ZnFGHZ8+ePcPDw3K5fMOGDRUVFWq1GsEhHxsrALWA0OMeJoCysrIXXnihq6tLFMV58+Z99rOfLSsrQ8oKl8v1D//wD6gN8sADD4RCIa1WS7MgUtQxpQBciYhj477ECflR/xNYzZ5qkAk2CIrzWAMMNqm3336b5/lwOFxXVzdnzhyFQjE4OIg6i4ODg5cuXQqFQtFotK6ubu7cuUVFRQaDYXR01O/3m0wmtVrtcrncbvfzzz8fiUQKCgqOHz++cuXK+vr67u5uzCGe53t7e9va2i5cuCCKYl9fH8wksVistrZ23rx5b775JvIbRCKRu+66a/HixchsSDmXXV1daCoyQ+/fv9/j8YRCIZ/P5/f7EWWCWrMWiwV+OkSq2Ww27Oxarbanp8discRisXPnzl28ePHo0aN0ASOS0mKxUOssUkEDZAOcAayMjIycPn3a6XQajUa9Xm82m6F1IJihra0Ntlir1SqKYjgc7urqQqyw3+/v6em5dOmS3W7v6upyu92ZTEaj0UCfRmwNKsU2NzeXlJSMjo56vd6mpqY5c+asWrUqGo12dnZGIpGqqiq73Y6MpTQOG7YQVg9kz1TK7GfRD8eETUtOTfZi9kpubGAi1Q2oZ4kekOFwuLOz02QyDQwMFBUVlZSUkOyuypIy6SyHFspx3Ntvvy2TySwWy6JFi+DqLS4u9vl8/f39s2fPBgOkr6+vsrISFcEApnFPmUxmMpncbrfH4+nu7h4eHrbb7Zhm+TISSiy7tEMw1iIT94x/KQcmk8kcP34clACz2RwIBJxO51NPPeXz+c6dO+f1esHw6e7urqqq0uv1HR0dhYWFDQ0NFovF4XDI5XKa8wdTXRAEt9sNnwAeBK4aPBuInIb+SSMTNBoNDN7hcJjjOCjM58+f93q9qEk3MjKCQwsalFKpHBkZKS4uRo02MAoikQiUT7lcDgMb9odAIICngN4G3E+xHSyycMpj7mWyZTInJaAbIRhaFEWr1UoI6e7uVqvVPT09x44dO3bsWFdXF1hPcrl8ZGQE+YjLysqWLFkyZ84cUOPgQSLZEG30DHgCudsoMITAJCh7V4/BJ80DgN2MZHUzgNSDBw+2tbWB9JVKpe67777i4mK9Xq9Wq30+H35Ivdjw2IBXQH2V9BqWysLuNsj8KwhCW1sbCmLMmTOnqqpKLpdfuHChra1teHh46dKlq1at0uv1SJ8lsU2869k5rkDp1Wq1n//856PRaFFREfY9lOaFVkyyGpGQDdmn5aiovRNrDZonfgVWm1Kp7O7uhrFp7ty5MAkhtzJ6DL6CRCIBnSGRSNTX10+bNo0Csqt4qY+BgECL/iFZJl4qlerp6enp6YEB5brrrrPZbMiSB5sFK1enEKKrwa3leX5kZOTEiRNwwi9YsABjikMfk+GjHpuBWQdVlhBCNdtIJOLz+R555BEY7xYtWmS3210ul0qlcrvdb7zxBhjXtbW1dXV1drsdbFLc8/2wj3xcbS4SW63ky1z53yBgLifAcVyhGI5iNWxk4XB43759O3bsiEQi5eXl69evNxqNPM+jzEpvb+9PfvIThBXW1tZ+/etfnzVrVigUcjqdSMUYDAaPHTv26KOPAnWBc4K0oYDI2A0PHz6MdJCIbU2lUps3b16yZMnMmTP9fn93d/dzzz0HG9KiRYtWrFhhtVop0xcRxoWFhaFQCEaUP/zhD93d3Xa7vb6+vrCwUMyWxMLZHwgE3nrrrUcffRSYRq1Wg3U6PDys1Wr7+vrOnj27ffv23t5ev98PpnUgEJg2bVpDQ8PKlSvB5UCOIzDUQTcqKiryeDzRaPT8+fPd3d1KpTIUCkUikZ/85CdwnIHKqdVqQR+yWCxqtRogHmoGcBLyWyOyAvsLtBqtVltUVPTKK68cOXLk7bffhv3VYrH86Ec/qqmpuXTpksvlam9vV6vVM2fOLC0tBRoDMYbkuIA4RqjiR08piQue/oSdVCzWl2gLFP3jdehcYmMGLl26hD1Cp9Nptdo5c+YEAgEET3Njo4rRsEAg4Pf73W53b2+vyWSaPn263W6PxWI6na65ufn06dP9/f1r165dvny5z+f785//7Ha7R0dH165dC4YoTDWINiGEqNVqoFtQzFHKZNx1IYzN8EPfkWcixSXrE1BVFMVDhw6JolhXV1dbW2uxWNxu94kTJ2CMR1yvIAgNDQ3I+2mz2QYGBh588MHy8vLvfe97cA0BQ4uiqNFo2tvbt2/fjgoDSPwH8GS1WtPp9LFjx1588cVkMllcXAwKHEojNTQ0dHd3nz9/HhW74WCpqqoaHh6OxWKXL18+ceLEunXrrFYrsieZzWZkWPL5fIcOHZLL5Vu2bLHb7ajlhHwA6XR6ZGTk6NGjLS0txcXFd911V1lZGYKPMdBer/fcuXN+v1+tVtN0RpjGk40BoAetxWJJJBKY5CdOnHj++eehhKOmAeVayOXyeDwei8Uef/zx06dP/9u//ZuYJb+BAABiLrQd5FlnJy0d3wxTZC13LUyJQqEIhUIo/Y58XAMDAy+99FImkwmFQjU1NfX19Vu2bEEKFJhOCCGo6KJQKKAwZLIlEQkhYMVQ+iiI+xQ907MpFov5/f5Tp07F43GLxWKxWOrq6vx+fyqVevvtty9dumQymVatWtXY2EjGMtSv8BzMJ2ibKIqbNm0CXw6GA7wOx1TShD7D8zylpWF7ROi/Wq2mRYVlMhkIVLAXCILwjW98o6ioKBAIZDIZi8UCmIWpiL3r0qVL0HwKCgrWrl2LEB3cB9HVn7QpCrWfelcw2Twezy9/+ctoNGowGGpqam699VZ4DoEWyNj9/Oq0JlEUYZcJh8Mej+fQoUNut3vRokXz5s2bOXMm3LMgiX08KgBQYh70GRrc4nK5tm3bptForFbrli1bNm7cKAgCdK1HHnnk9OnTWq32nnvu2bx5s0KhQAwGNza8UHy3QKkpuQqRSyz9EvyXK3QMWHMmoo6efvppZPLZsGHD5s2bEbfBcdy5c+f27dsH6hsMdaWlpWq1OhKJFBUVaTSaw4cPP/vss0NDQx0dHTNmzACgAQMnGAzqdDqXywWD6HPPPdfb21tUVJROp+vr6//v//2/tbW10Wg0FAp973vfGxkZSSQSsFnefffdhJBQKIQsioQQFDZCUoienp6HH364r68PNcI2bdrkcDgAO2jU+fHjxx999FGO4/x+/6233rp+/Xrw1YqKirxe7w9+8IPR0VGEL8NmfN111yGKtKioCJQbmFph2oTJGX5AVB0nhIDSHY1GCwoKLl68yHGcxWKheUIRmRoKhbAkEP2MjMVgCnV0dIDoDJKP1+sFD8Tlcv3xj390Op14rsPh+PnPf55KpU6fPn38+PFt27b5/f7rr79+5cqVhYWFcAeDsyFmE5aRsdCfgh5hbEIYdsIIY7NniEyICP0JeyvJUyiPn/5QEIRkMnn48OFQKOT3+4uLi1esWIHQZ/BzsNfQM1XMOiUEQWhubkZQx/LlyxF1d+bMmSeffLK3tzcajfb09CAt96lTp+AiAHvKarW6XC5EKuOYRLQ34lVCoRDJr0xTT5rknABAoV9SryiyPuMtwDpraGgoKipC+lfQ1aB24kVSqRQy6vb09IBp1t3d/d3vfvenP/0pQhTwq0Qice7cOeQ8gRttwYIFVqtVLpePjo4ODw+fOnWqv78fhP5MJlNfX//Zz362sbHRaDS++OKL/f39MMbr9XpokqgSgPdClEg4HPb7/TKZrKOj44knnhgYGIAyZrFYvvKVrwSDQSw6q9Xa2tr69NNP79mzJxqNVldXL1u2rKCgQK1WDwwMPPbYYydPnkQ0MOjagiBs3rw5ky3gOllxuVyEEFRLOHTo0EsvvXTmzBmazo8QYrFYiouLa2trS0pK1Gr1s88+29PTY7VaFQqF0WhEYA9stKBjATCJohgOh5PJJPX85E4AycSeeP/kPnzBke+3YEuBsR9RSe3t7dgxZsyY8aUvfQkJ2aDwI5wdwUggjkIJR9wOTitoiTTqnV1ftP8VCsWBAwf++Mc/RqNRjUZz6623zp07NxQKnT179sCBA5FI5MYbb1yyZIlOp4vH4yaTKZMtdYf70NHMB/vyjVcqlYK5BwgegVvYujkmsIrCvmAw6HK5UqlUc3PzgQMHnE6nXq9vamqaMWPGihUr4CLDK1Nq3Lp162w2WyaTgfbOcRxM/rJsTmGfz7dr166TJ086nc5PfepTa9asQe8RprriJy0MgBr+kU2SpuZD/I/FYikrK0MaEoxLbhgAu2CvXKCjwnp44sQJUIVvuummhQsXwlFPH4TnXot3/SCFz6aZxsEHfJVMJh999NFDhw4lk8ktW7asWbMGamo8Hv/Od77T0dFRUlJy/fXXb9q0CfZKaF8SQyHJH1t4FZJv/X7Uh4BuWSzQmmAfGz8GYIIHCNkQJTHrx8ev+vv7BwcHkY+ltrYWVASZTOb3+y9durRv3z6ZTDY6OlpeXr5w4UKUGs1kMqBkDA4OoqxpWVlZe3s7jHOpVGrJkiVQIhUKxc6dO5ubm/1+P7IGNTQ03HbbbSDrgyWCZut0Ohhx586dC8M2vhFF0WKxeDwepNZ58cUXBwYGysvLnU7n5s2bDQZDKBSi5wqQNx4nk8lgHigtLUX6CIRItrS0wLDU19c3c+bM9evXz5o1q7S0FBktkskkYBBwWDQaNZvNhBCYdbmsL9hgMIDwgO0YZxWwOBJTEEJAEGcpjHjTkpISmE5HR0ddLtfSpUthMDabze+8887w8DAeBNMXOAlKpfL8+fNIUH3TTTfV1dXJ5XK45KLRKBKq0vzukmOV4nsydkHSNUnpQHT+sc5udi5KQBK13cJyRrKICv7r06dPo5hDQ0PDxo0b/X4/OC1sRV76CI7jtFqt2+1ubW1NJBJLliyZPXs2/nvgwIFz585xWbcvSMAYrJkzZwJYAHzo9XqdTgefA1wNaC2gZL6DU4Ln6L9UAWD7kxACp//g4OCZM2cEQSgoKKiqqnI6nSjL8M1vfrOrq8vj8SBE22az+Xw+r9fb19enVqsLCgr6+vo4jvN4PNgxkTILJaUvXbqUyWTcbrfNZrPb7Yh2UKvVp06d8vl8LS0tiMwRRdFkMs2fP3/x4sUwEBJCzGbz4ODg3Llzi4uL58yZM23atG3bth07dowQotVqUaXYZDL5fL7XX3/94MGDFy5cAJfJarUWFRWNjIwUFhbC3B4KhVpbW0+ePOl2u6uqqrCnh0IhhBd3dnYGAgG5XO7z+TiOc7lcFy5cgAIAG8FkOcpmsxnG5tbW1r179x4/fhwZkPx+f1FRUWVlZVVV1fTp02tqalB6r6+vb3h42GKxrFmzZsGCBYFAgDrQaKkaBMbI5XIARG4sd5/+m8nW+6PTeGIdhmOSzIhja0h//AToB70H4tmOHTuwmxmNxtWrV9fU1BQUFLS3tzscDo7jTCYTArECgcDQ0JDH4zEajaWlpaBuFxYWKpVKVhnDbdH/1ClNCAGzLhgMYkPQ6XSIP969e3c0Gq2pqVm/fj30ahqnSNtM1+lVoAEkcsHhSNMKQaURmMxpqHkE9Wb//v0nT55sbW2F6aGzs3N4ePgzn/nMvHnzgO9J1rCaSCQSiYTNZhNFMZlMIlcBnMwAtXSnHRwc9Hg8NTU1a9euhW2L7rewIn3Ugc5kBacnNRvJ5fJYLHbw4EHwHmtqaurq6hDaRLkr1+S5cB0TQnw+35tvvnn58uXS0tLZs2eXlpYiXgvuCHpWflQqqecTgC7MQ7w44q+OHTuGnN1btmxBUju3293e3o6CsPF4/Gtf+xoQmt/vZ/lXuUbDKZlY2DOFe7eEZvJ0ttAVGQvR8tFwqYGWZE07OBefffZZLLBVq1bV1NT4fD5Eyj733HO7du0C3RMu/nXr1mGLF0Wxvb0d2zQSZTY2Nn7961//+c9/nslkkBoCO/vDDz/c1tYGZ2hJScmqVauWLVtWUlKi0Wiam5s7OzsfffRRg8FgNBp9Pl9RUdGCBQuQ5BFhN7CpQ11xuVwPPPAAwFxHR8f8+fO3bt1qNBpxbEciEbVaDRjxxhtvgLtWX1+/efNmnucHBgZEUUwkEv/v//0/m83mcrnMZnNjY+OXv/zlBQsWgPwARiZwPzYR7PJerxfHnlKptNlsgUAAtgew5YLBYGFhoUqlqq+vr6ioADPHZDJ1d3e//fbbvb295eXlq1evXrBgQSaTmTFjBtKK/eAHPxgYGDCZTNdffz2yvIFt9cwzz3i93rKysmAw+OCDD1ZUVPj9/pdffvnEiRO9vb0qleruu++ura3FoSiKIjQNbmx9X4najcbz2aQcIhPvS79njaD82Hw4dPXmKgD0FKf3xId4PI5kUIi+2Lp1a0lJCcxgIJlQi5rA5KSKRCKXLl3yer2EkNtuu62wsPDw4cOPPfaYy+XCUyKRiNls7u3tff7552Ox2NKlSzdt2lRQUAAgq9FoQGEvKipChiUwypCjbYKFREG/pPdYIEv9J6IoIuCkubn5ySefTCQSy5cvb2pq0mg0TqeTELJ27doFCxYgdazJZAqFQjqdLpFIIPVtW1vbf/7nf8JzjYAHt9t95syZ1tbWs2fP9vf3gz63bt261atXW61W2P5/85vf+P1+aKdKpfLGG2/cvHkzzKvnzp3r7Ozcvn078mt95zvfgYnx/PnzhBCXy1VVVVVTU0MIGRkZCQaDhw8f/vnPf15SUoJzFDoSZoLP58tkMqdOnXrzzTfPnz+PVKHd3d2zZs1yOBzt7e0///nPXS5XMBjEvg9PSzKZ7OnpAe/zKvg/6Nv+/v5t27a98sorCEpG5oAHH3wQipDZbEbdaOQOjsViFRUV119//Z133llYWIhQClSdo7WWmpubu7q6ysrKFixYsHLlynGfC34RWIWEEERFa7XafArMJ80DIIqiWq0Gco1Go729vW+99RZsOv/xH/+xYcMGpVLZ19dXX1+PXM+pVGr37t3vvPMOkhwAE5tMppUrV5aUlMydO7e2thaLEeQZBKXE43Hs8yQbhiEIwptvvhmLxWQyWVlZWXFxscvl6ujoOHnyZFFR0dKlS2fNmoUcRHACsJYLiRVtUqJQKAKBAEIYuawbkyawJtkYd1iRRkdH//3f/72/vx+VH7FYDAbD/PnzwYRGYBKMBdifUdcMwTmEEL/fjxLgVK8IBoNer7e/v9/pdELpxWvC34hXgyvmGo/0h1s4poi7KIqYim+88QbCJxYvXrxx40YoUYiDulb9gwTifr9/aGjozJkzRqNx5syZdrsdRjdsFOAjwXf9UWe5iNkcHvhvIpHo6Ohobm6GTfPWW28tKCjwer2dnZ1ICVNYWHjzzTdv3LhRLpe7XC69Xg/CKo4ArEqqHX1cN8lrKJP2ALCojsVSLLeVDgNEoVC43W6Hw+FyuRBemUqlzpw5AwvNHXfcgbv19fXt3bv3rbfeCgQCpaWl4F9OmzatqakJoKGjo+Of/umfqqurfT5fYWFhfX393/zN37hcLthyUCjA7Xb/x3/8RyAQSCQSZrO5vLz885//fHFxMTj6Z86ceeSRR0RRhKUWZMrGxsZ77rmHcklhbgflNBAIgPYN2/+NN954xx130BJm0E/OnDnz2GOP9fT0AGI6HI4HH3wQIAyGfKRHjMViJpPJbrffdNNNTU1NJpMJlQpCoRDAEM0liqBk2GlopbN0Om2325EM/utf/zpM0SaTSS6XFxUVweRPCBkdHXW73fPmzRsYGNiyZYvD4VCpVEjhv2PHjkAgAM7ipz/96YqKir6+vqqqqgMHDnR0dCB1/de+9rW6urqenp4///nPLS0tsVissrJy8eLFN954I2IqoH5EIhGaBwNp+IRsHkOaC5mmRETjxSxHgg3Iwx2ooUuSD5hnQvhZoExjCeAjgmcD6H/btm1IcKTRaObMmVNSUgJlCfGCiUQiHA5jQ4ENe3h4OBKJvPzyy319fatWrZLL5adOnfrjH/8YiUQcDgfP8y6XS6fTBQKBJ554AmPx6U9/GklCxWwUAeVi0syzJBtDRhmN9C0o+Zhnaiawi4UbG/cMlAyfj1wuP3v2bCAQKC8v/+xnP4tOKyoqEkUxFotZrVbMebPZDA9+KpUyGo1KpbK8vBwqOvg5e/fufeqpp7RaLfATWFKEENSV4zhuaGjol7/8JaIjDAZDbW3t7bffvm7dOmTjbWlp+e1vf9vf3w8Szg033ID1JYqi2+1uaWnxer3Tpk2bPn26x+O5fPnyyy+/fP78eQTnUCZGY2Mjsugmk8lTp0799re/dTqdgiBotVp41VevXh0IBP75n/8ZVgObzfbAAw8QQtra2n73u9+ZTCY45ZETxmKx0FgLCRQTRVGhUHi9XijtSMibSCQQRXP69GnMVYPBMH369K9//evl5eXIvQjgBY23sLDws5/9rFqtrqioQL1w7Dk8z6vV6n379r399tsIF1EqlaWlpfv370eFWugGiMV3Op1I13v06NEDBw6gwFxJSck///M/05KraKrVag2Hw7TEoSiKIKbDmgCaOGwNNIEBzfPLZUOQEfODccEUBUUe9gVkOMA1UOnhS8H98VdCCCYAGClCtragXq8nhKAGC8gk9NBFtyPvGW0e4GYmWzhPzOaWxRZKCMHYJZNJOhXlcnkikXj66aefeuqpcDiMhbxkyZJAIACbK0D83r17Dx8+/M477yAnLMg5IyMjgiA888wzNptt+vTp//Ef/yFky5IEg0HoFS+++OKePXs8Ho9er7/tttvuuOOOoaEhTBilUllSUlJaWtrf3//kk0+63e7f/OY31dXVZrMZPlJAwHH9MBMADqxlZJbEcgZxFHkCWCSHdLcYYqVSCQfsn/70p9/+9rc6nS6dTut0Op/PV1ZWtn79+vr6etQrhDUHlnvasaCDIjQO5CKTyYS5DRULYQavvPLK8PBwZWWlw+HQ6/XQ0qlxB05FqE9gKyUSCZPJFAgEcAKy+zN9XziQ0STUxqF0mnz9w67fd+3P91vi8TghBOtCrVZ3d3d3d3dj1D796U9v3boVRwBix+HvhQEb2hr2VfQ5ySYqoMsQiRBg0SdZFZRks8ClUimPx/Pkk0+q1Wqj0Xj//fc7HA7k7cATQXj7eMQAEEKgtPt8Pqysb37zmzKZTKvVbt68ec2aNYIgPPvss3v37o3FYgsXLqyurr7lllsQGGmz2eRyud/vRzIJoAva4YIgYOzi8TiseNiXOI5DOmnMf0S5YDSxN+KvhJBQKIRhErLFZ7BIsTODTQ36CTR2HN9YL5TRBAcFxUXQ4rBdY8tVq9VX4WGD+sdCLEII5YaQ8c7Bce9DJx67ACdojBx/ptCNUpn5bMYA1uQPwWJALKbf7w8Gg93d3ddff/1LL700c+ZMg8EAY+rIyMj27duRTqevr89gMCxevHjp0qW9vb3FxcVyufyhhx5SqVR6vd7j8axfv37evHlYcgCmZWVlHo/njTfecLvdIyMjgJtbtmxBHCGSRTzyyCMA3DBzIp/mihUrkFiqtLQ0EAggsIzjOOQAOXDgQCaTAf136dKlSNOGMhw+n6+np2f79u1nzpwxGAwIdkSVPugYRqNRoVCMjIwAMXMcN2vWrLlz58pksoGBAYwW0rezxmn0Hg5m8NfFbHYLlUq1fv36OXPmYKnodDpBEPCvWq2GMZjjuI6ODofDYbPZCgsLOzs7lUol6sADHNTU1CAsGIksg8GgIAjwDFRXV588eRK0ELlcXllZ+eUvfxksCEx3pADimABfMUvC4bOJ7el8ok52kUn2Tx0CdLaxK5bkmPwlc5HeFkoFIjjNZvPIyMjg4ODg4CA6beHChXQRwlgC/AEAhOLQsVhMq9V6PJ5kMokYjOHh4V27dl2+fJnn+fLy8g0bNvzxj3+EbwEZQnU6HQqRwHHPZTP0iWODHNgP/NhaJJT/zdIP2LfGrMgwRZ0hRqOxr68PpBpchmBlEBVEUUTtC5oQHWEJNpvt/PnzAwMDOp2uvb29oqLi6aef7urqQpCZTCYrKipC7k7Euzc0NJw5cwak/+7ubnjhPvvZz86aNQvZD0HmQVT9nDlzlixZMn36dOxoyWSyt7e3p6ensbFx/vz5oVBoaGjoxIkTJ06cgPsLUZWJRAI7PjL9v/7663v37h0YGED3jo6OQqdVq9UHDx5UqVR9fX1Wq3XDhg1YmDt37oTLZd68eRhHQOF82TCA9VEwxOVyyeVyi8UyMDBw8ODB48ePowpYWVnZ/fffP3v2bErfAtjFPAdunjt3LmUholvS6bTH4xkcHDx06NCRI0dQXI8QEggEotHooUOH1q1bh3FEctXBwcF9+/b19fV1dXWhu5D0TKVS+Xw+Wi0RMxx831Ao1NfXJ4ri9OnTkfYb9ebQPIPBAO0CSn4wGERgNw4VygUXBAGvgMmGJPGA5oFAoLCwMJFIIKUGSH3YIXGqgYgIgqJCocBtw+EwIQRVR0BcAa4lhGD3xvYCyIgeAKcFawR1SBC3ChzPcZxerwfpAsvW5/ONjIwcO3ZMEAS73b5w4cL169f7/f6SkhKsvs7OTp/P98wzzwwPDyN0FbuHVqs1mUxQ20AbwGmNs7ygoKCnp+fll19+8803wUZTKBTt7e3xeBwD0dHRsXz5cqvV6vV633nnHafTWVBQQLIROxijfEfjxCJmS4IgtYMoiqhmg90buxaCgHmeB/4ghLjd7kQi8frrrz/33HMlJSUIgiSEVFZW3nrrrbfeeivKmQMgYv5wHIf0biiaCX0AN8RTCCE0PCyTyfj9/paWlnA4LAjCrFmzaM0TjinOyHGcx+Ox2+1IkGoymaBr0eIe7IkvZnluJMtEwtrMZGs7XF0H/pUFSjJUPmhou3btcrlcc+fORUQiyXInKMk2nU4jQaeQTXjIZYOD6YEIxgsiWAjj76VdrdfrBwcHM5nM7t27Ea9YWFj4UTfzTyBw4SKXdDqdbmtrU6vVgUCgqalp9uzZVqv1yJEjJ0+eBBtcJpNt3LixtLSU4zjUb4Gxhp7C6GF64GILQiJ4eMPgS4Q7hRCCPQ0XA+LT5CLUEIw/gfWA1CkKhYJmyAUPHCBBEASNRoONDvYdPAKxN8lkEumk6bNo+4VstokrFzFHyFiUL76bLf/qRM4ziVwk9kt6drKNgP0ylUohoDaTyRw8ePD06dO7du2aNWvWmjVrYOQ4fPjwm2++6XQ6LRbLyMgIBqa0tPSmm24SBOGpp5564oknkLonmUz++te/BngdGhp68803RVEsKiravHnz7t27T548GQwG7Xb7zJkzP/3pT8OaxfM8andj+Ovr65HhBPxdUAscDgchBKnuse3u379/x44dgAjxeHzLli1r166FywnEhscff/zy5cs4rTGEW7ZsueGGG2bMmAHgDlySTqcLCwuRjbG3t7e5uRlwCnYaOmaIgxGzllpslF6vF5DOZDJZLJaqqqrrr7++qqqK1jDCkU8IQaxkPB63Wq3JZHLJkiUGgwElXUOh0Isvvjg0NBQKhWbPnr1x40YkZEwkEs8///zBgwd1Oh0m68mTJ8+cOTM4ODg8PAw9e/bs2ciOh3GEhg2FWMxaqXkmqJHPZpej6jIZm9CTzkuqKFJndGZsghR2HnMMWUhgKEbQsHGy7tq1q62tDRm1b7vtNgwxpWLjeuAnsLojkYjL5Xr99dc7OjpKS0uXLl360EMPwQawdu3aefPmLV68+Ne//jWMWFD0V69eXVBQgK2HNd6I+S1VPFPKQLIUOcbpRtcUbS3tYfzJ5/OhIF0sFps5cybtXlB9gKph6gBO9Xg8xcXFly5d+uEPf3jixAnUNOjq6urv74eCgWxRZ8+ehXY3c+ZMhULx9ttvP/XUUxcvXnQ6nVartba29pvf/KZGo4Fl+i9/+cvFixd7enpQXOy6666bO3eu3W7HJIzFYmfPnl2yZElLSwtS5v/7v/+7x+PB+vqnf/qnmTNn/uxnPzt16pTdbr/hhhuSyeRDDz109uxZ0PBge4YHDMrDsWPH0ul0VVXVrFmz4HkzGAznz58H/l65ciXQLRt+x6IQfEYaXHyJ1FsnTpx46KGHMPGKi4srKyvvueeepUuXJhIJquiSrCqLdY0wA5h5MLf9fn9XV1dPT893v/tdxOiLWS+W1Wq1Wq3PP/98dXW1QqHo6+tD7i9RFC9evAhLrV6vd7lc4Gs5HA5QU0ZGRoqKigoKCgYHB2Ox2KlTp37/+98jw9I//uM/gjQol8sRaE4IiUQiAwMDsVjM7Xbv3Lmzp6dn/fr1t9xyC2sTou4jsKdgL0AcOaqYDw0NFRcX4zRCcDlMjCCT4BSkhxaANc6/ZDKJzDlarXZkZAQGzlAoBKBMsgku2ZIOJFt8iuM4xKyjGDNhCqKBqi4Iwvnz58+cOROLxUpKSu68887a2trq6mpCiM/nc7lcf/jDH06cOOHxeCwWS0lJyZw5c9auXYt4GHBjfvGLX0QikZ6enpaWFkB/1Ov4xje+gdh3lLlIJBJDQ0MvvPAC0sva7fZAIDBr1qwXX3zx3Llz6XT6lltuqayshDMH2xSLMK5cUKFSka1B7nK5CgoKcHAg7IEQAgVMEAQo5wqForu7++GHH4bG6HQ6zWbzpk2b1q5dW1dXx/M88o9BH4YFFCEoJGsg7O3tRe2a9evXm81mcE2pcxWPdjqd3d3dOp3OZrPhOKauSMKEOnAcd+jQoX//93+vr6+/6aabFi9ejDbnGhrFbHIFfA/NBPPhIxRJHIlEsNASiURXV9fvf//7Y8eOlZSUbNmyBexEamASsoXb2YNbzGZuwMFKsh2CVYMDiFrNWDNZLBYbHBzcsWNHcXGxRqP54he/iA782Nj7JQL3IKqO9vT0PP3009gZNm/ezHHcwYMHH3300dHR0SVLlnz5y18uLy+vrKyk1neSLRwuY+pyiIxFksv657H/0Jwo2O6wZcEyAtUaejLYR7iAJoUPhUI8zyOaiFo2kQYdRwkdaOTBq6ys7OnpwVYPAxzHcfX19ZRCTy2GmavKY8HiqFzUxIrARGO+d5FGUop5KET0e0EQwJNDcp54PL59+/aOjo6ioqJNmzbNmDFDEITf//73KFVoMpluv/32l156CcbsLVu2tLW1PfHEE/39/Vqttr29/Stf+UpDQwPq4QmCgAQmhJCFCxcajcZjx46FQqFgMLhixYqtW7ei+PnJkyd/8YtfYK8Mh8P33HPPwoUL33zzTdi8sWjBj6QavMVieeSRR5544omCggLYF6dNm3b77ber1erBwUG32/3aa6/19fXh2MPkiEajixYt2rp1K7RMQRBMJhMMY3q9fvr06bBFdXV1IQB35syZqIeK8xLaJAAxzldCCNiuyDKESGWlUllRUSGKInI4AI7gcShQcuHChWQyuXLlyptvvhlnHiFkaGiov78fmRxvuOGGOXPmYI0ZjUan0wnqBV7/2Wef1el05eXlmzZt+sxnPqNUKouKilBlDHoUsCNdY3To2c8YGpqBDmZyulFKNAHCRImIOamBuGzaPolPCT+XyWTUl8dx3P79+xE6sm7duqVLl8LIjTaji+DIhhkPBoB9+/bt2bNHoVC4XK7f/va3PM8nEona2trPfOYzFRUVQ0NDlLoAZX3z5s3gDkHpZ9sjMIHL7Ad65kmWqDxbrkhiOYMFQswyI1n3CE6jdDp96623FhYWgjKBVIkqlQoABfY2UJhOnz6NnAmotmi1WuHRdjgc06dPv/XWW0VRfPDBB2GOuu666zKZzNtvvz06OgqlF7l31Wp1WVnZyZMnX3rppdOnT6tUqrq6usWLF8+YMWP27NmgGAHBBAIBt9vtdDp1Ot2OHTuefvppj8eDhIb/8i//AtJ2Z2cncMP27dtdLldzczPgLzLrDw0NPfnkk8lksq6u7rnnnovH48uXL7///vsbGhoqKyu7u7sPHz4MHlFBQYHNZovH49DYhfHonrQ/USUKpYtPnjy5c+fOoaEhURRVKtWdd97Z1NRUXFwM7CtmCyopspnUabSo0WhETAVcdocOHUI+UMxzBARDn/d6veA6+v3+4eFhRBEIgmCz2RDs1NjY6HQ6DQaDWq2ePn26IAiIby4pKQmHwy0tLdgh+/r6sEUEg0EQRVKpFPhRhBAEBb3xxhsvvfQSgp10Ol1fXx8yQYGjqFAozGazIAgejwcmFbq3wKwgiqJerz979mxvb69er1+6dCnP8zgLURccWzfyHaFgNiEE/kxMP1EUL1y48Morr5w8eTKTyZhMprq6uiVLltx+++2YluDpUq0AFi+U7QwGg5FIBLHXimxBK0EQurq6nnvuue3btwuCUFZWZjQap0+fLpfLkQtIo9H813/916VLl/r7+9Vq9dDQ0N/93d9t3LhRr9dHo1E6XVOpFKwker2+uro6Fott3779kUceQRqDUCj0rW99S6/XP/roo4C86AH086lTpy5fvhyJRBYsWLB58+by8nIaD5rJ5iyfrFHWbDaDVRKNRp988sm3335boVB8+9vfXrFiBVUAgOnRCeFweHR09Pvf//7ly5fhn7z33nuXLVvW0NBA/QOybJpOmj0Cf3K5XNB1f/WrX7W1tQWDQYfDAZ0cGmAkEoH2hSoi6XTabDYvWrSoqalJZBJ2s4iqubn5mWeeOXPmTG9vbyqVamhoQCg/i+nZjR3aCDURggIBqsak+u2DEmynsC6l0+kDBw4QQsLhcFNTE1KTiYzhllqI0+k0/GPIzwE/M7VbgbhI48jZE43awiKRyOuvv37q1ClRFO+7776VK1eCJ5bPw/lRF5gSkHnl4sWLzc3NRqNx9uzZr7/++vDwsMvlEkXxtttuu/nmmxcuXAjnJMLYMNVhnaGJEDkmAABrVhAE4Cv4XrD84clEb4tZ2hWgIFoFxRUnKUYWJDrcEPo5HV/8CkY6nU6H9N9PPPHEgQMHRkZGZDIZylI1NDTcfvvtjY2NM2bMgPZIFeOJEfy4wuIoCD824Qq52jxUE0veuEaR8QnQlwFswjoBC6izs7O/vx9O3oaGBlEUz54929XVhfw5GAOoZbDTdHd3DwwMoLjVihUrFi5cWFFRAW+pKIodHR0owlVZWfn666+jEpZWq4XNBqkMX3vtNTj9CwsLlyxZsm7dOofD8cwzz0AXRGUrHPbBYLCysnL//v3Nzc27du2yWCwA6AaDYebMmRaLBbGSe/fubW5uxpHmdDoVCoXRaLRarcuXL0eQLgh/2NNR7Wj+/PldXV0dHR0wD7z++uvpdHrlypVImAjCBsAu9R76fD6w1XHoiqJYXl5eVVVlNBqxg8iytWxJNlCsvb29u7tbFMX58+fDlIUpjjJkoGzOnj27sLAQyNjtdqMmAPA66t2WlpZu2LBh6dKlCPdEtKKQrUBJ2fwU8YtM/K7IWKyhKgjZOCqqPMBDyh4w+C91pLI357LOOHZy89nUnzQkNJPJ9Pb2Dg8PQ1FZvHgxnE6iKEK/xwkEh6BarQZHU6vVHj16NJVKmc1mZMOcM2dOZWXl9ddfX1FRkclkRkdHTSYTokei0Wh5eXl5eTnHcPSx+0y8YASmyDGrHohjPRv0e0weebaSFM/zmO3gccF1OG3aNEIIGO34AOMrDMmCIIyMjGAD7ezsJIQgHkMmk6lUqqVLl8Zisa1bt86ePRvEm3PnztXW1i5atKitre38+fOBQMDr9ZaUlFRUVJjNZq1W+8orrzQ3N6PacSQSWbVqFbrIZDJ5vV4xS8rq7+8Ph8N2uz0ajY6OjiIqpr6+ftmyZdXV1SA8oPxcMBgEZ8ZsNqvV6oaGhrlz5xYUFLS0tKTTaa/XOzo6CnCMYkxqtdrtdg8MDLS1tcGBPm3aNLBvYUJGKaVx+58a52QyWV9f3549ew4cOADuypo1azZu3AjyHuZVMBhEzwOm0L1VEASfz4eZGQqFenp6Dhw40NPTo1QqwT8EHyaZTKI2eVlZmd/vHxwcDIfDVqsVJm1kLVu9enVjY+Mvf/lLTMjFixej0jnWGioA9vT09Pb2gmKEuTEwMOByuSwWC1D44ODgwMBAS0vLsWPHEB4QCoXQPzD2I9SH53m/39/R0YFCfuvXr7fb7ThEwWxpbW11uVwvvvhiX18f8q2hVQijR9p48JWLiopABILHCTSVkZGRbdu2tbW1NTc3g0UZi8WGh4dFUVy3bl0ymTQajcjyNDAwYDAYLBYLFFT4yg8cONDa2hqJRG6//fZp06YpssnmL126hOSwsMAhygVtRuqq5uZmvGZ5efmSJUuw3mG7IYQMDQ01NzdDP8Ru2dvbu3///v3792O7EAQB/iue5998802Emb344ovQCb1er9vt5nl+yZIlq1evLi0txb4HjzHJ0lomqwBkMhnEd6ZSqSNHjnR1dcnlchjyMcHgRMJ+otfru7q69u3b19/fr9frg8FgQ0PDLbfcghAdekwgSAAzVmR87zhl+vv7Dx065PV6CwsLHQ5HUVERpd+AJSWXyz0eT0dHB96ovr6eMhsJY6TEf41Go9/vt1qtKpVqYGAAhyZN65crYpYWj5vQgnqT6rQPUGBdisViZrP54sWLiURCp9OZTCaYCYBqqAlfJpN5PB4oBuhD1OqB6xiKdCAQQGgNPcXIWJUJp55CoTh69CjsEevXr4fdDXTBD7ZD3ieBTVCpVO7bt+/QoUNY7yaTqa2t7eLFi8lkctOmTai/gWwoQFYIAMNmxXEcLZZKhaJQujTC4TBs/NSwCC86piXCmaLRaH9/v0KhQL4KklUkqMMBpzPN0JjKluZEOhDAsIsXL7rdblixNRoNMF46nXa5XLA9WSyW0tJSSl7is8kJJtVv4647yZfc+5ArQs5COpLFf5T4wU5oPB4WdBgkMpnM3r17BUEAJi4rKzty5Mh//ud/Aufdc889119//R/+8AeQSt1u9ze/+U3UuwUf/Utf+hKXZQjI5XKbzbZ9+3bUK+3t7T1//jzigEHnMJlMu3bt2rZt2+nTp+12+6xZsxYtWvTZz36W47jh4WFCSFVVVSAQsNlsqJ0EHLx3794f/ehHsVgsk8mA76XT6T73uc81NTVduHDh+PHjSFBVWFjI8zxIkJFIJJ1OIxVDdXU1Xt/n8w0ODsK7bTAYli5dOjIyotFoUC315MmT8LAvW7YMxhsKf2FNRIByJBJpbW2FtczhcNx+++2LFy8GzoAxj8sG/EFfAi+8tLR0zpw5PM97vV7w6k6ePInqwnD6p1Ip5J24cOECAhMB7GBp+/KXvzxr1iwEMaPQLCHEaDSOjIzAiwILOnvkcNkUTyJjyydjDfkUMdOpyY31GmM9U9TL6pCSOc1lM+Uh3w442a+//jqAYENDQ3V1NV6KPheTFmOKuB9CSCgUwrg7nc7i4uL58+cTQlavXn3nnXeC8oFCEIQQpVJZV1d35513winMon/avHFRfm6z2V2JjNXRxazlDOZnalpLpVJIgfXcc8+Fw+H6+nrsKWVlZb29vQDfABBDQ0OdnZ0XL17cv3+/XC6/fPkyDM8IQDebzXfffffq1asRHAyobTAYNm3aBKX66aefhnJut9vvuusujUYzPDyMACyO4zQaTVlZ2W233Xb33XejKAEMJ4ACPp/v9OnTyWRyaGjIYDD09PSk0+kvf/nLGzZsKCwsLC4uHhwcRGGEwcFBrDu5XK7RaDZu3Lhy5cqGhoannnrq+PHjULNBQFq2bBnKv2N2vf3227t3785kMitXrkS+JqwUhBjl9jk+I0qY47hQKLR79+5jx44NDQ2VlpbecMMNW7ZsMRgMSGSEwEeQfQkhAHx8tpQs9C7w+A8fPoz7IKAcGw4SqsL0kEgkoHuL2bjPjRs3btq0ief5mpoa5M+NRCLBYLC6unrWrFk4lkRRPHXq1H//93+3tbVhc4PdfWRkxGKxgEiq0+mi0ajP5/vd737X0tLidruBUXA+lZSUIA0api60pp07d+7atau3txfxiCtXrnQ4HNFodHh4+Ny5c7t37z569OjIyAhiOl0uF07BSCQCZWnnzp0+n++666773ve+h4gmGCYCgUB7e/uzzz775ptv0kg4So/BcsYy6evra21tPXr0aH19fVNTEwwusVjsnXfeefXVV0H5Xbx4cXFxsU6n43keJwI4lqIoogrHl770pZKSEkQEXrhwAUdPRUXFqlWrqqurUTOe53lAf5/Ph/2ztrZ25cqVw8PDbW1tr7766sjISHV1tUajAVEKSdx/9KMfabVaq9Xa3NwcjUZ1Ol1PT095efns2bM/+9nPFhcXQ8PE69CTbtLHJiGwWYZCoZaWlnPnzhFCwDcjjNcUJedbWloCgcD3v/99t9sNt9KSJUv+z//5P1VVVZg5drudEBIOh10ul9VqlclkMJ/RjDQajaa1tfWtt97C5oBAOxppLZf/by7v3t5e1JMpLi4uKysDO4UaOOhWLIrivHnzli1b9tRTT2UyGdT5njVrFvJY0B1MslfjmPD5fLDRUM7MVfTeX18QLIFi8L/97W/B0/jqV7+K96UKmJh1mMBA1t7e3tvbC1/l0NAQx3H33XdfWVnZ2rVrcaSypyT2bZLtN1isjx8/jqo1d911F5AS4nM+2N54/wSEQ4/H89prrx05cgSOQaxim822bNmyr3/96wgBhfrE8zyl4MIuxmczCtJTlWS1dBzTFy5cOHHixOjoaFVV1R133AENDb9llVK/33/gwIF9+/bp9fp77rmnpKREr9dTA+Lo6CjW3ejoaH9/f1dXF0DUnXfeCegiCEJfX9/Q0NAf//jHM2fOAKYiK2BFRUUsFgsEAt3d3YODgytWrCgtLSXZAEhRFIF4J9Vv9GWpzV1iBZAsyWulCcip4ssCO3ZVs5CIilwuDwQCXV1dR44cEUVRrVbPnj37oYce8ng8Ho+ntLR0/vz5d9xxx+joqNPpxISA/zoSidTW1n7nO98pKipCrBsgKY6QdDrd39+/YcOGs2fPajQar9frcDiampqcTud3v/tdOK/r6uoqKiruv/9+kHNQyspqteIkppFzTqfz3/7t31wuF3ZhkCWUSmUwGNyxY8e2bdsQxKxUKo1G49KlSxcsWNDf3x8IBPr6+ubPn/+pT31q4cKFsPGjOBdcfugHs9l82223NTU1/fjHP75w4YLP5ztw4MDZs2e/973vLVq0CAqikKV9A61aLBZwnFD+2mKxVFdXo4hjJpNRq9XQYuFwoOa6srKyzZs3V1dXwy+m1+uHhoZgZkMVgnPnzp05c+bcuXMtLS0ymWx0dBTaDs/zDofju9/9bklJSSKRQLQKTbAdiUReeeWVioqKGTNmlJWVofhAJpvWA8OdYbJ8UCzLMY4qPputn6JeVkPlxlJOBab8jQRS0/kGhRuFzPbv3w/V6N577wVVGiwphOQDzMFeBedJJpPZt28fdAZCiMfjQUyh0Wi02Wyw1cViMZjYsSOsW7cO7gsyFtBjGeeDBSLDkeMYrzq79bPLh88mAqIxBjBCuN3utrY2i8WCdIQcx4XD4XQ6XVRUBCR36dKl3t5en88HcxT0nOLiYsRQfuELX0By+kwmU1dX19XVZbPZqqurr7/++ltuuQXesEAgADqQyWTyeDw///nPL1++jEfI5fLbb7/95ptv1uv1HR0dc+bMATqhwabhcBhl8hBaHYvFGhsb77jjjsrKymAw2NvbK5PJzpw5g0hZmUyWSCRmzpz5+c9/HqbfcDh87tw5r9eLZCwmk2nLli3XXXddVVUVbOpIZgqtY/PmzY2NjXDNI6SbZtjgxvIWCCGYBq2trc3Nza+88kooFKqtrX3ggQeWLl0Komd1dbXf70eylGg0igQ1fFZEUURkxfnz5w8dOnT48OGBgQGUlSgpKdFqtbfccsuNN96YSCReeOGFY8eOeTyeUCgEKPzggw8uWLAAyQAKCgqQ2IfjuEOHDvE8X1xcfOuttyLpEGLxn3jiie7ubiz/66+/fsGCBW+88caLL75oNptramqw9tva2trb2/fv3w8q/KZNm5YsWdLc3Lxnz56qqqqbbroJu1Yikejt7T148OBbb7115swZv99fXl4OV14mkzl8+PDhw4dPnjwJOzTdPZArCXFBGAhQ/BFEkUwmoQDH4/Gnn366paXlyJEj6F5YsEpKSkwm06xZs0AlSqfTHR0d3/nOdxB9dP/99yPa7/Lly0ePHn3++edRfMNut2M+BINBv9//4x//+PLly3q9vrCwEIW98L3H42lubkZmFexO5eXlL7zwQmlpqUajGRgYgMEC7x4KhbZu3VpfX2+32x955JHLly+HQiGQ12+55Ra9Xo/MacifE4vFvF4vkPHg4GBRUVEmk/nc5z5XW1sLeJ1hSq+QbCjtZI3Z2LS7urrOnj0rCILZbL7++uuxUmTZiiiIGHnppZd6enoCgQCqzv3zP//zsmXLAN8FQYBrNxaLIYQA1kcAI2wXWCnPPPPMW2+9RWmfdrsdxk5kkkGHx2Kxvr4+JLdZvnw5SOeyscklKaro7+9vbGzE0RYMBltbWysrK9mFlgs44Hxubm62WCwNDQ2NjY2U9vDhF5wmw8PDb775ZmdnZ01NTWVl5erVq6kHGx2OlS4IAjTVZ5999tSpU6BvwR73ox/9CNGrTU1NsBlReiGEHosw0r344ouhUGjWrFkoSRmLxbAnf1QUp8kKvGo9PT3d3d0+nw+ksmAwWFBQsHDhwi1btlRWVsIQABs/TAxAEdTujOBSbP7g/kUiEYRmoczR888/HwwGZ8+eDUMMjnWSJSbQJA3PPfdcS0uLRqO55ZZbQLKlPIuioiKO444dOwa64+XLl+Vy+bx586677jqMb2tr629+85uBgYH29nabzRYMBhcuXLhgwYLy8nK73X706NHnnnuup6cHeiAlNkPPvAr3Dsu1oZ9prAgF3rm8oPcoY1yE7L9UGcB1tAUYObBgL1++7PF4kGDbaDQi1rCwsHDu3LkrVqyA55rjuIGBgZKSkkAggAQXt912G3LaBAKBUCgE4/fw8DDc2YSQqqqq8+fPx+PxiooKQkh9ff0Pf/hDn88XCoWmTZtWVVW1devWoqIibH/IZlBdXZ1Opx0Oh9frPXHihNFo3LlzZ19fXyqVgsd51qxZJ0+eRK6Djo4OQojD4QiHw+FwePXq1Z/+9KeNRiMcrA6Ho7q6Wi6Xo4QQgocEQUDQczweB01Cr9fX1dUtWrQILn5BEJDlg2MqL2B3wLAhPzoSCKLHysvL5XJ5OByGgY16jbEHlZWVFRUVoQBWIpHQarWCICASlKK0zs7Ozs5O5BmIRCJQrGFSUiqV/f39LpcL3gz4E9rb2zUajcvlam9vf+GFFxYuXIjco7JsxD3H8PvZbZFkK9Lz2Tp/YjZhAlA4BdAUsXFjOW10cuNNBSZMCoJEInACID2OzWbjOG7t2rVer7e0tBTIPslUxMQZTAhB3pVdu3aBDYxogfnz55vN5qKiIsSiIEUmlAGe5xcsWIDErKz3gyo2VAEQc8z/Yo5PQ4JQubE+BHow0I4CEh0cHBQEAf5HEH5aWlp0Ot2+ffuOHz8OuxGgKsqgEkLUanVJSckDDzyAGvJIeVRUVBSLxex2O/LhIKzZbDa/8sormMNKpXJ0dHTnzp0UmsyYMcNisTQ1NRFCCgsLYd5GVDG45pjnvb29WI9Yd1u3bnU4HCMjIw6HA50GEINa3eXl5WD0Ye9OpVLd3d3Q+VHUvampae7cuYjcUiqVZ86cASqqrKycMWPG6OgobD9w6VJLZK4IgmAwGA4dOvTaa69xHLdo0aJUKjV9+vRkMolNJp1Owy/vdrsR7s9umkgAhWDx48ePA3qC11tVVQWOeEFBAZIENDY2vvnmm8gJlk6n58+fjyLfWE0oW5NOp3fv3o0Gg8iE3HAnTpw4c+aMSqVChMbq1avNZvPp06eR+dFutysUikuXLr322mtnz571er08zzc0NCxcuDAYDJ45c8ZsNiOYIZlMWq1Wt9u9b9++119/HfFnUNXKy8vNZvOlS5deeumlS5cupVIpi8UCP08ymayqqhJF8cKFCwcPHkT5CGQZgvIJ2gniGXp6enbs2AFmGpgtICuCoHjdddfNmjUL8Q+HDx9GrWi9Xl9aWmo2mwcGBs6dO0fNQMhHZLPZECp34sQJ+EUvXrxIg/tHR0eR4Q2V4wRBKCws9Hg8PT09xcXFw8PDCM6GLx7GnZUrV959990ul8vj8bS2tur1+tra2lmzZjU1NaHOeigUogiP53kUmxsaGkJSgYULF6LYMDYx2OfgB0AnkMkXGUX+CafTOTw8jI5duHAhrDwkm2keofbnz5/v6OgwGAx9fX1f+tKXGhoaRkdHHQ4HDDpwBMG0hLTOyCMEPyFiuJubm48ePYrrtVptfX09nAM0+5lMJlOr1X6/3+/3+3w+OE5pHBHdhEmWhInAIVAu0Rgkw0VwEX1HFhLALnPgwIHf/e535eXl991334wZM66VGfKvIEiLBHc9cEVjYyP4b0I2RQxhUtt1dXVdvnz5woULw8PD6HYhm5ocpDKSjQNGlBEZa88CHggGg6dOnYIzp7CwcGhoCFwDBLd8gL3x/kkkEonH42A6EEJAIkXy8aVLly5evBhuEzA/w+EwzLjwGyiVSlSAhbmWy9bLgycc8Zbl5eUtLS2IJQBJFTR9MHDYmPtoNHr+/Hn4qZDKLJ2tB0UIQa6FXbt2gUkINiMojqIodnV1tbW1dXZ2IhkxIgnvvffe6dOn47/z5s07ceJEf38//KVUx8auotFoJhsbQ6MxOSYMnX6gsEFgGMjXROTQgNHd0FyRHBr6GTWLUgAH15jT6Wxubn7++eeRt3FwcPBXv/oVCHbr16/fsmVLRUVFIpHweDz9/f1IeJ9KpW666aabbrppyZIlMEWXlJQgOBgm3nA4DGo7Et3Ait/Z2dne3h4IBFKp1LJly2688cYFCxYgaBi/0uv1Pp9v2rRpBoMhGo2mUql/+Zd/ARPAbDZXVVWtXbt28eLFXq+3ra0NAT1KpVKr1TY0NKxbt2727NmwQikUCo/Hgz3XarWWlZUhtWgoFDp16tTs2bNFUbx48aLVasUpAviCPIA2m21kZESv1zscDkSAwV4C0iTHcci4jHAr/GlNthq2KIo0KgAnaCaTQXnj733ve9jZAXmxiSsUirKyMoCboaGheDxeWFhICEE2vRtvvLGjo+PYsWMOh6OkpOShhx5C4Mv06dPnz58/Ojp64sQJhUIxa9asv/3bv62tra2rq6M0CcqOoO5+QDfKqCZZBjylynAcB/o1zFEcQxZC8Cvs/fSvhBBWoyUMXMaRrFar//znP7/++uswLn7qU5+SyWQFBQVgSRFCaISQRqNBjldYCMDohX8A6V8+9alPgb+BXR6jDCrRQw89ZLfbOY5D/AB7jFGDPSLqkAUCRcR0Oh3SL0qWEF6B7gL01bCkwRkF/ALzB2Uc9uzZU1RUFI1GX3jhhZdeegkpjPAULEAkQ+B5HimtMFfnz59vs9kEQQDf0eFwUNVCEASdTrdq1Spsgg6HA+lckKsXOQQzmcz9998/f/78mpoahFIlEomysjJE1gLFAgLK5fLh4WGYc0pKSh588MGZM2cSQurq6kZHR+FNWrFixSOPPFJYWLhs2bLPfOYzYCVxHIdMl36/H9E+JSUlX/3qV2fOnImulsvlXq/317/+NfKofOELX1AoFLW1tZlsjmd0AtjzmJZIpQ+znE6nu3jx4rZt27A/fOUrX5kzZ47NZtNqtaOjo0VFRYQQZHCDVoPMhsA6fX19b775JiKPkWQJRF4Ayn/6p3+y2WwajQZ52RHn4Pf79+zZU1BQsHbtWupElsvl0FKwax0/frygoECj0TgcDofDcfbs2Z/85Ccej0epVN56660oMwdSLA48FB5GTGdzczPyrv793/89Ip2+/e1vwxOybNkyQojZbN67d29/f/8zzzwTDAZLSkpaW1u1Wi34e++8886vf/1rlGCvrq5esmQJ6BxarXbZsmWPPfZYR0cHrP5yubywsHB4eFgmkyH1IfIrHDly5PHHH29pacFEhf4GUF5UVHTPPfdga3311Vf/8Ic/IMgEZv7FixcfPnz4jTfe2LVrFwJCsH2tX79er9efOXMG+ZSKi4sRuMLzvMfjUalUtbW1giCAvYMTVK1WI0U68usRQq6//nqn0zlnzhwEdWAEL1y48MILLyBxxxe+8IXGxsaKigooYzzPI61CKBTauXMnsp9hqaLcL8ifCCOGTxiQ7l3Df+FRAWShxhGYCbq7u1944YV9+/ZZrdampqaGhoZ0Og2fWCAQCIfDTqfz//v//j9EmwSDwc9//vO33XabXC7H+UWVEKxfbPXYgbHFof1vvfXW008/DX5CSUnJN77xDeQyRyJC7DCCICCyvLW1NZVKORwOURQRDIObyJm66XiWRqOpra1FJEA0Gv3xj3/8u9/9DhkRYJjgspmp0D9DQ0Nf+cpXvF4vKNTgqoFfNCnA8X4LXFU0kQDcWWAnjo6O/vSnP4VB94EHHpg1axb9k9PpRLATXEOvvvrqk08+2dPTg+rU0Wh08+bNK1eu3LFjx6lTp4AXQ6EQBho5KJG1D64bBHk7nU7Umly/fv11112XTCah9RFCQHrBgQ4qL5cNBSHZMwhGFlxGc4QggJVWQcHmL5fLYdsGeAC1Bt+zBjvKoqExr5iuuFhgQiAo2JsAa+JgAkJA7xFCXC5XKpUKhUIvvfQSsFNtbe3y5cvnzZs3b9686upq9DCsPDQeEto+FHisAtQk4Xke+cH279+fSqX8fj+ABGZ7aWmpUqn0+XzpdNpqteIoB07Q6XTPPffcjh07wPiFYRT8bWTAQ4TVqVOnXnvtNfSSTqf713/916KiolAodPr06f3792/btg0m3WnTppWXl99///01NTVQ9i5cuADXMSHE4XDA1ELzOOMUoxFQbE9y2fQqSHaC5tE3SqVSp06deuedd0DTmDt3bmNj47p163bt2nXy5EnUQVq5ciWMesprlH5XLhlpaqSkHCaJCQHvplarjxw5gvpcoVCosLAQiV2XLFlyxx13zJgxAy4C5OJMp9M1NTVLly695ZZbkMkYtA3MQrgpy8rKDAYDdImioiJAE+xEYNDef//9FRUVtbW1sLLASQ04iK1Qr9dT6lU6nZ49e/Ytt9wyc+bMkpIS6AMrV65sb2/v7+9HNNjChQtRU10QBIDsr3zlKyMjI0NDQ2+//fabb775gx/8wG63Nzc3P/XUU0CEqVQKxwmoZvF4HImD3G73smXLUHSdso15Jo9qJpNxu91ILoTC7DfccIPJZIL1FKHG8CIhawQhBLhcZJg2OBgymczatWuHh4e7u7sx72UyGXKJzpw5c9q0aT09PX/84x+Rchs6tF6vHx4ebm9v1+v1q1evXrNmjcPhQDFCECQofQWDwjMp8DPZdPtcjkeYy+YMpZOJG0sBkvgE6Pe5n/FoqGGwywaDwXvvvff222/HxiQyzDS6hNBdSPLldrtHR0eRnXb58uXwQcHaSv0bc+bM+bu/+7tYLLZq1SqUzaJOKnEsGQmbPvwGTzzxBBII/v3f/z0A97gLiRtLVqFqEuYkjiX8KRAIXLp06cCBAyiVgpVP8w17vV7km0omkxaLZfHixatWrVqyZAlY4Fj2SqWSzU8scbNgqqxZs2b37t39/f0w95aWlt58881NTU0WiwUzEM5KjC/OEpqCFtY+DEdZWdmdd96J9EGwRhQVFcFCWVtb+zd/8zfV1dVVVVXTpk2DsSQajVZUVPzsZz8LBAJ2u12r1f7gBz+oqKjQ6/Wjo6OwSra1tTmdTnA8zGYzJj86jcbsoj9hBsY5ge4aHBx8+OGHEbRXW1u7atUqlACHbx2BEKIoFhUVwUOCSmEggzqdTkwtQohKpfJ4PDNmzEDl8o0bN+J8ha2hoqKitbX1hz/8od/vr6ioUKlUd9xxx7Rp0wRGED25fft2zDHM1W3btv3yl78EnWnjxo133XUXDM+XL19Gddu6uro5c+bs3r371KlTXV1dq1at6u/v/+lPf1pWVqZUKs+dO9fT06PVapuamurq6qLR6GuvvXbgwIETJ06gPlpdXV13d3csFtNoND/4wQ/Ae3Q4HLW1tVu3bj116tS5c+egSG/fvh0kpdra2mXLli1duvSrX/3qtGnTXC7XHXfc4Xa7g8Hg6Ojor371KwQnYCevqKhQKBSzZ8/+0pe+VF5ertFoDh8+fPDgwX379vX09GAQq6qqvvGNbxw+fPipp55yuVyBQAAnen9/f0NDw9atW//yl78MDQ3BY7lhw4Y33nhDzOZE/+IXv3jDDTfodDqDwdDZ2fnAAw+Ew2Gv12u32xcsWHDfffeBUVZcXAytHqeAUqlsa2vr6Ojo6Oioqqpavnw5ir0ghDocDgcCAVEUh4aGdu7cuW/fvpGRkQULFuD1sbHgdIB/ZjLn4/8wudnNgWSJNIFA4OzZs5lMxu/333rrrZWVlTD3AMnBvYPC8JlM5hvf+MacOXPq6+vBY6R2ELDscLBirybZVHttbW0HDx7805/+hIPDbrd///vfN5vNqHBPAS4NYwCxihCSSqVMJhM9gyT7GzXEgJj31FNPEUIikciBAwdWrlwJJxIWOLo3Ho/7/f5vf/vbiOKIx+Pf+c53kPVO9uFLZylmk5fQz+iiUCh09OjR06dPgzSFbF1QpVBsHnY3QRCOHz/+i1/8AvlqZTLZ1772NVQoGhkZuXz5skajqaqqqqiowLBSvDQ0NPTMM88ACi9ZskSj0Zw8eXLXrl1Go3HFihWNjY24P8kOOobbYDAg8xusUbD0pdPpSCQyPDwMqMfz/Lx58wghWKEKhcJqtZJsVAPIwHDewl2M6iVcNhM3EBSWEjXPEUIQSQXXExTRSfUzKmN6PB6DwVBYWIj4pYqKCiRbR2EZjuPmzp176623NjQ0+P1+BH9GIhGkBpbJZOgHlGDHIvX7/YFAALS9b3/72729vcFg0OPxIK4J511lZSWqhsOtB8sRHJtFRUWwyPz5z3+GtUir1c6dOxfPtdlsIPG7XK4HHngAzKJUKnXdddd95zvfQRDXwMDA97///UAgAC3XZrM9+OCDZWVlKpUKfJNnnnnm7bffRuKE+fPnL126tKysjKIgGoqQr99Ai+J5vre3F6Q7j8eDvLQdHR3oVULImTNnLl26dOjQoUOHDh08eDAYDO7evfvGG28sLCysrq6G1XXSa2M8+V/iI4vGxmU4QJBnJhaLnT59GuZ/cGRlMtmKFSs2btxYXl6OqQb/Mixqc+bMWbFiRVlZGWK3i4qKvF4vIQQRqOASVFRUbNy4ESG89fX1g4ODsJ0YDIaKioqVK1dqtVqorbg/rBRQfJFIBPXPkRGvoqJi7ty5xcXFVMm+6aab4BcuLi6uqakpKCjA1qnVavV6fWtrK1aOIAhmsxnmjYqKipGREUII0vyBq43CtF6v1+l0iqKIePOmpqbVq1dT1x7FfzT6E7k+kLKquLi4sLAQ0X6AdNR4LDDlqIRsdh0uy/0CUFu2bNmpU6cGBgZsNpvBYCguLkaca1lZmUwma2xsXL9+/cjICCysgGUKhcJgMFRWVtbX11dWVprNZuzs8NNhiwRBn2Qj5enoi2PzAtEJQ80VkinFNpiVfGoA/gQXLXRCsAuuu+46KJBU+SSMJg3bP4wZoVDowoULnZ2dmUymsLBwxYoVq1atQuYfGJJBpykpKVmxYgUyAcNZRN9LcnOO4+BP8Hq9x48fHxoa6ujo+NrXvpbJn/ea2szoDXkmbzHJlkeFzRiuANg+RVGEJQ83RyLOTCZTUVFxww03rFmzxmKxxGIx1AYC8CWEgGOKeFm2f+j6dblcQFqnTp3ieX79+vXr1q2zWq0FBQXoEJHJ1AQaK2LKsSqdTqfNZlOpVIsWLdqwYQOcszKZjJrKYGi5/fbbkUIbyzCRSKCYKJpks9lmz54NZwsWMkIO9u3bB6NvMBhEMpN0Nv0Idh5oRDjhAoEAsjRgV0VyUqQAvuWWWxwOB+ibOClFUbTb7UhKBhvMwMBANBrt7e1FDh9ER4hZhgbyACIlBbhY2H+dTufJkyeR1LK0tHTFihXFxcXwjeBlYanq7+9/9dVXseQXL17c3t6+Z88enBMlJSWNjY1wckaj0ebm5tbWVmRvjEQi77zzjtvtLisru3z58s0337xgwQIc9keOHIGKMmfOHJRL37dvH9IhoMDZpUuXYOgaHBxEPDQhpKmpCRbo3/3ud4FAgOM4g8Hg8/kqKyvVajUqYKB6Wk9PT2FhIboItbThS1QqlSjICN3prrvuKigoUCgUJ0+e3Lt379GjR1FkVxRFnF6O/7+9745v47rSvTOoBAtIsAFgp9iLWERJpCotUc0qlp046xbHcRw7if2U3cR53iTPm02yL3E2v332L+vkbcpbx3FJstbajuRu2VJULFmyREqi2MReQYIgQBJsAIF5f3w7Zy8HAFUsuSQ4f+gHQsBg5s6de79zzne+Yza/8soriEGkpqaingEFRW63u7W1FZQkuGHgfU1OTq5YsaKkpCQjIwPuN5IM09PTLpdrfn4eWg7z8/MWiwW7A1CR1+uFo9Xa2ur3+0tLS1etWgW/MSYmBmqwTqfz7Nmzhw4dOnnyZFxcXEFBwQ033HD27FlIKmk0muHhYYPBcBU9mPjYP2a1T+4k2t3djd0Q1wLxfszbiIiIlpaWxsZGyEYlJycXFhbGxsb6ZI0UQD0sYsgPY01gjE1PT09PT58/f/7gwYPHjh1rbW1dvny5w+FITEw0Go2oY0YIDI+wX+7ojAI8jUZjMBhQYheY3yAfAH4pHEJ88tixY2lpaVarFdlXQWbFtLe3nzt3DpuXJElf/OIXi4qKEhISAEavtNjxozGEDrGBqtVqjM+bb76JEQMfD+Om0WgAEjwej06ng7+t0+nUarXdbr/jjjtWr16tVquNRuP+/fsh+5GRkYGMk1qtJumO5uZmNEBMSEjo6+sTRbG/vx8rFZicKpUK/gZSVehgA08eVU9IPsDPhyTGE088Ybfbt23blpWVhaQfcVNxL8BNYoxBFkKSVY+dTieSsbhG7MU4VbfbDSwLn5kxhoQe9Uy8zBHGOSckJOBPo9GYkJAA5N3U1AQmW25u7o4dO9D+DMRIVGyieQJyiVgcqPsNCJatra0dHR2Tk5Mul+vs2bNWqzU5OTk6OhqPG2h+GEDGWEJCwujoaFxcHNEN2traoP+G4CDCtRDswmi//PLLoB5FRUUlJSVt3LgR6x6Ylgg9zMzMlJeXp6ampqWlYZnq6enp6el58803L168aLVarVZrYWHhmjVrQLIgn1zkZEB5FI0FxGQygUabmpoKaWmtVrt3796LFy+izsThcMzOzloslujoaHQKAnHdI/dbJJVSHHmRLM3l2IIMAO8gUoiCLRS3Aq4aGBgAPREQX6VSPfTQQ2vXrk1NTfX7/WBcICz0m9/8RqVSIaEDMQe/3IUK7imiCACpgD46ne7+++9HRhgk6SVLlqDrJ07PYDCAV6fVasfGxhAdeeCBBxhjUMFHpLagoADMewTkli1bJggCJZKQDGJyR8OKiorW1taioqLc3Nxbb701Ly8PheqlpaUlJSX4aagVjY+PowFQcnJyYmLi0qVL16xZU15eDpIZsnV0gRRxuXDhwvvvv5+SkoK0DmSkKZpId5FCFwgjSXLhLHIyYPbHx8d/+9vf/ru/+zvMb5RpghOCUrP169eDuIKqI8DHgoICbOr4MGY5xkEtd6tFIFAl18nRydO0VsS5A+P9ZHwagf+XDsjHpRhjmCQ+n++ee+7p6enR6/VLly5FipY/DTom7ggqnnt6elC/UV1dvWbNmo0bN0Jqhliz+BWAHsBfzCtB7uzIP6V4PT8/b7PZhoeHL1y4QEW6iDEEfZB4t5lfRtVyfwBJLh6IiYlZunRpaWlpQ0MDChLwBIExv3Llyo0bN5aXl+OmgK4GcSSdTmc0GuFUYM7Af6BhFGUNO+Bgk8kE2j2eLLjiKKUC5xJ5BnJRsEYzxrxeb1ZW1kMPPZSenm4wGBISEhDp0Wq1IL+Kogh4bTabUaYJ9VW4/X19fTfddJPH48nOzr7hhhtUKpXBYAC1bH5+/tChQxcuXADinJmZSUxMhCAPXGW/XEVHZ4XJDxx27NixP/3pT319fX6/n9p05OXlGQyG0dFRBHgcDofb7Z6bm7PZbPv374f/BgeYMQaNL4PBgDqchx9+GLWhs7OzfX198AH+/d//vbW19eLFiyMjIyUlJffff39eXh5CyBLXVHhwcPCll17CXHrooYfq6+v/+Mc/QnVg+/btGzduRI/wuLg4j8fT0NAwNTWVkpISFRWFPmJGo7G4uPjhhx9GJiE+Pt5msx04cCAtLU2j0eTl5T311FPnzp3r7u5GoO4f//Efm5ubkUYoLy/HbhEREbFmzZr/8T/+R1RU1OnTp51OJ9wq7JHr1q0rLi5euXLl2NjYz372M5fLlZ+f//Wvfx074oULF77xjW+AyzE6OqrT6erq6h566CE04jAYDB0dHXv37j116tTo6Cjm4fDwMDonPvroo5IkoZVhYmLi6dOn3W53VVXVtm3bnnzyycbGRpVKlZSU9Mgjj/zqV7+SJEmj0ZjN5j179iQnJyNHiu7sdXV1b775ZkJCAmSXvv3tb2PSgrYLvNvb29va2nrw4EGVSgUhqZycHI1GMzMzA47Z2bNnH3/8cTSYv+mmm7Zv345y2yNHjgwNDTmdzpmZmVOnTkEaa5HgXFDDDsUWtjb3+/09PT2oytVoNPn5+YLcyhSxWLCVgCM3bNiwYcOG7OxsPHGU48IKD+0gAHqDwTA0NLR3797XXnsNzDfQfqDl9eCDD6JBMtZtUTaMp8fjsdlsWDHgHTHG0O2OLdzBYciHFBYWVldXo81FV1fXiRMnKioqRFEEV212drarq+v5558/duwY4KbZbP7CF76A5xFu+SctA8DkIILIadaNjY11dHScP38+JSUlNzd3y5YtuK1oZG61WsFmaWlp2b9/f1NTkyRJFovlvvvuKykpQd/D//iP/wAV8DOf+Ux1dTXIpfDfQGew2+2AuRMTE6dPnwaHWa/XT01N/du//Ruo6oWFhenp6cuWLauqqgKtEUsikqszMzPgLc/MzPzDP/zD2bNn+/v7kSM1Go1DQ0MxMTEAyohbR0VF9fb2/vM//zNC5o888gj60DHGiALkk9XzsFPMzMwMDw+/9957oEKAQaDVau+55x7o2MAuxw3Aio1nYXZ2FoSfn/zkJwMDA0wmfaxZs6a0tBSZVVEUR0dHz58/DwmB1NTUFStWwBkm8R+73R4TE3Po0KHf//737e3tdrvdYrHk5uZaLBZIAz/zzDNIQiIPBtCFEAM87fPnz/f29h46dAjKZpOTkyUlJdXV1fHx8ZIkTU1NjYyMDAwMvPDCCwMDAygcRbYH6cRTp049/vjjjLGpqam0tLSvfe1rWVlZg4ODjY2NR44cOXr0KGNMp9OtWLHC4XAsXbr0oYceQgU/biKTFXshZcEPJuGKqakpvIZ44969e7u7uw8dOoTk4ZIlSzIyMlD4dPbs2ZycnIMHD2LfiYyMHBkZQZKE6IKBx79SU7OF6J9xzVD5g9IKgs1gZmZm2bJl9fX1JpNp69atS5Ys2bFjx+zsLORWgJZ8Pl9KSgoQAxqaarVa+A9AmfNyKwdQVIHpJUnSarVVVVW4YERc5ubmkPbisSByavHx8UjHpKSkSHLdtNFoRB0JIDvihZiscEuwOiD6glAHyDk/+MEPnE4nPEX4YW63+5vf/KbBYGhtbX3ttdfApkB0MzMzMzY2Fnn5mZmZkZERKOsJCxWscOFxcXG5ubnFxcX5+fmIOIKqjuVDlPurUz5XkGtBJJkRgaNBBVKv18MpjIuLA4cbCB5LAJQiRkdHs7Ky2tvbJUkyGAwgCwHuo60JfhS/iP1AkpsYIHvIO4GhDHeEZqEC1vNAH+YPkLWiIDRjzO12I28DxotCmoPgtSQrbaENKpLUOTk5d955Z2lpKSoxUCQHnEoXBQ4luL+MMfACWcBTBMgCcAPVlM2bN2dlZaF/xeLjwBvexIYNtwoBj4qKih/96EeAVoIgREZGJiYmQrAIwTk8gIAUWN2wqYtyzzJAbXiAdP5+uekEk2NgkZGRSFgh7uKT67zVajWISX65NxYQttfrBaI1mUwbNmwAIRuhPniYYOMAXiCXQr+okltKo/7kq1/9akJCApZFVOIivXvkyBGXy4WPlZeX45FhsoADVlJRFBHtYIzhHKanpycmJp5++mmHwyGK4tq1a++44w4sESaTqaury2q1Dg8PgyZ0+PDh559/HvRQTHvwfJqbm5uamlA/nZmZWVtbe8MNN/T19blcrqSkJI/H09/f//TTTzc0NExOTup0uttvv3316tU33HADBW4RfEW0oru7+/Tp08jXv/LKK/X19Xq9PiIiory8/Ctf+QqG3WazWa3WoaGh8+fPz8zMNDU1oT61pKSEMXbbbbdFRkaiG0BfX19TUxNwldvtfvvttw8fPiwIQlRU1N/+7d+WlZX96U9/OnToUFNTU1FRkc1mQ+CqoqKirKwsNja2r6/vhRdesNlsmEtqtbq0tHTXrl1Wq3VsbOzrX/+6JEnp6elf+9rXzGbz6OjogQMHXn/9dUEQYmNjJycn77rrrpUrVxYUFKAtmtPp/MY3vuFwOLq6uhB/wb6lVqutViuEeicnJ7Oysh555JHvfe97aPwcFRX105/+tL+/Pz8/PyYm5sknn0TGNSYmRpKkbdu2rV69GuQWzBmz2XzLLbfMzs6++OKLbrc7OjqaiEmIdyChBG1czAE8Ji6XC5jj/Pnzx48fn5iYGBwc3LFjx5YtW2JiYgoLC+Hz//3f/31bW9svf/nL6enp9vZ29GW7UgeAD81gf8HKDHkQTIbi4mJEbWk9OXbsWF9fX05OTlJS0ubNm8vKyhB4wsojyMLHKpXKZrOhGGPfvn2NjY1o44UCFRKfgY9x2223gaxFqVoYPGQmSzxRPQ8CUgIn5qFwA/R6vdls3rZt2969e0dGRkRRbGhoQAcJ+Jbd3d1Hjx49dOjQ8PBwZGTkt771LRSEJCQkIPVENRufKEPIH69FUYRQ1Ztvvjk7O7tkyZJdu3YhbAFnCZxen8/X2Nj4yCOPINLh9Xofe+wx5K8aGxvffPPNAwcOoP3C17/+dbQTVfwi8aawwiNeq9Fo0tPT7XY7tuampiZol3k8noSEhP/5P//nsmXLUFOEfA7UZn71q1+98847oigmJyffc889u3btstlsyEDSRQmC0NPT85Of/KSrqwvLFxSukPbHQoqJgfnj8/na29svXLjwr//6r0NDQ1i3EWONi4vDQsHPDQX8CzTsI1gKJiYm/u///b+NjY1tbW3wghITE7/1rW+VlZXFxcX19fWpVKqOjo6urq4//elP3d3d09PTmzdvLikpwRaPrQH54ffeew+qwVqtNjs72+FwfOYzn7nvvvv6+/sfe+wxg8GAGAfWkDvvvFOr1XZ2dmZnZ4NzfubMGai/4JJ1Ot2yZct27drl8/lGRkbi4uJef/31V155xWazxcbGWiyWr33ta1ANttvt77zzznPPPYerKywsxLC3trYePny4vb2dKMEqlQo618uWLcNsAY5CQgMPJpoTK8YTgxYZGYnSMrvd/tprrz3//PN4iHQ63bp167Zv315eXo7H9vOf/7zNZhNl5ToqQSRUQDfiQz0s/FEIsgiC4AtobgdDEAI6mFAj3rp1qwIYYYIiuKjX6xGtNBqNgJgIxAqCgII/DBPeAVhH73d452hgiSSsz+cjrjCTS6QB32dmZkAKNxqN8KrxVCPlhOmOZBxQDvAuvg4qPHh4YH2NjIwgG4W2HQh1pKam3nLLLWgO0tvbm52dje/CcY+IiLBarTxJQxGaLSoqSk5OHh8fh3iI1WoFXOMLbnjPgUdyEic9CRQyPT1NgmIulwuDDF7y2NgYWHEYt4yMDGA7jAmId4LckRegED8BdoTX64XQ0PT0NIpyyCFkC0P+odYIctIUbwY6A4xbaHAmTKZgUUEqPUg8+meMoVoRdef5+fnbtm2bmpqqrq7G1PXL7cYQG8OUAGAFCmdy7CqQ7YazpXZON910U3d395YtW8RFFbio8IPuGn/7sApDqs/n80VHR8/MzCxduhS+NGMsOTkZZ4KoEpKblD3AM49IA36Fni+EeSS5kAOv8YtgI4Bfgfg9HG++XhkjADwH9hEqmXDA0dFRdGuCT4XMMiKOGEl63qlCQCUrMSOkBKklTLyYmJjp6WmwFLKyslatWlVbW4syMqyheGbxJ1LMCKXgfo2Pj/f3909NTRmNxpqaGpDoIiMjQZdC6m96ehqVqT09PYgSQZ+0uLh4enoahQdqtTorK+uee+4pKyvr6emxWq0TExMjIyN2u72xsfHYsWOo/87Nzd2+fXtcXNzQ0JDVagU/mHKGBoMBrvXU1FRZWVlLSwsKjr1e786dO8Gzgsy/Wq0GSSkrK6u7u9vn8+Xn57vd7h/96EdocNHf349rP3DgQH9/f09PT3FxcUNDA4ofTCZTZmYmXLKBgQFslnq9Pjk5+bOf/WxeXh6Qosvl6uzszMnJGRgYwL1D/pox1tPTo9Fo2tvbQVyen59/4YUXXn/9dXjOaL+1ceNGuA041O9//3toYMfHx6M0+ezZs5hsFy9ezM7OPnPmzFe/+lXQmUA99/l8Bw4ciIiIKCgoWLp06YYNGyjaAp7xLbfcAl4ZYwzTwGQypaSk3HrrrcePH8fKgDrdiYmJiIiIiYkJg8GAOoSLFy8ilZ+Wlub3+3t7e7u7u0+ePHnmzBlBEMD5KS4uLi4uxlYKtI08cEZGRl9f39mzZ0dHR/1+/5Wqr/g4OQ5iZvp8vqNHjyJNil2MeDjIhjU3N6emptrt9urq6uzs7K6urvj4eJ1Oh1+HBgZmS1tbW3d398DAwHvvvQdXH/mQtWvXer3eU6dOIf4HXhBaK1Dk1Sc3xUOAA+2TQGUB+gEMYgujLbThQoA1JycnNTW1t7dXFMWBgYHGxsbU1FS0ODh69CiyKNCSXr58udVqxRKHPdFkMn3SMgASp2TNGBMEYXJy8tSpU83NzdHR0evWrYMCLyplUQIHfo7dbne5XDExMaOjo0giqdXqvr6+5557Dr3w1Gr1+vXrseeCsIqfQCYHpQWS3PkR0BZlSIIgJCQkoA8pYludnZ2oGaWNm2BMT0/PW2+9BWgeGRmJjoo+n29sbGxiYiIuLg7kSUSyGxsbh4eHQUMgAV/SfkWQHrWLbW1tr7766rvvvgvdMywjIOUvWbIEcTEKWvGjFyrghZUQ4QPUTIJuh4hDYWFhdnY2oAhUfV999VXU8Xu9XrVajaowRMEZYyDkHDt27MyZMw6Hw2g0Dg8PZ2dn5+TkbNmyhTGGduYonFCr1YWFhbm5uTk5ORg6gK6333779OnTFy9e3LVr18GDB8H7WLJkCYJfarXaZrMdP3787NmzaE5cV1cXFxcHgVFRFPfv3w9SFtDC+Pg4xDngL6F7Q2Rk5Pbt23fv3q3T6dCakDjbpF/EGEO+JejkxIaIRaO5uRkTSaPRbNmyZdWqVbm5uVhnUBk1ODgIqkhUVJTRaMzPzx8YGEhJSQHF5nLA2CVNHfhNQk78eRPwQr939PXEPpeenj4/Pw/uGhIfoiji+RkbG0PsECADzE4m92Fmci05kvLEnQImwGlgKiOeQQwBEuKgE8O8By5BvBOhGnArgZxouQReoQZYIIjji1AQB7hRySrLWE9BF8P6AkAGlXGUROO6IHmEk1dz4lCMMej3xcXFwVMHPkP+gWrw+Vvg4+Sf8CZg39zcHKiEyLEAuAuCQPLtFB8idxm0P5A+MRqCzO/EkCK4i0azra2ttbW1RUVFCEsIshyBFJBpUpywYhbxD4BiTVH8C4N3iwgryI5wP3g3mv8hqltljKWlpe3atcvlckXI3R7weJBrgQukvRyXj9nFU4Do+IIgYPSMRuNXvvIVNDHBaIRaEJERpmgcv+PigLQcA7gDHiUmJsLvAkkacVDQYwjBow4SWz74QmAREJuOHyJ6KFCmIooiRKJGR0fn5+eRLqPZBUICvkJpaMwfuGEYPfSgnZ+fR39crOnzXCsGr9cLmI4wGPQx4AyD4WAwGLDjWiyWmpqaiYmJtWvXrly5EhWfeEyI0oDTI+qCy+WC+3T+/HkMiMViycvLQx3C/Px8X1+fIAj19fX79u3r6OjAyRuNxqSkpKVLl950003Ilf/2t7/t6+tjjMXHx998882VlZUjIyO4y01NTadPn3722WeJbvSZz3zm85//PJSLtFrt+Pg4lEDhnyCV8corr+D5OnnyJKKGRUVFX/ziFzdu3IhAA6iGbW1tr732Ghi3qNkoKCj4wQ9+AL8IgNjj8fzud7/r6OjIzc11uVzAAVarNTs7+5vf/GZSUpLdbn/hhRcEQdDr9SkpKb29vf/yL/+Sm5sLdpMoitjY8vPzRVFcs2ZNTk7OZz/72fj4+N7e3meffdbtdmdnZ1dUVPzjP/6jVhZxBqxEBeHQ0NDo6Gh9ff17771Hz290dDRUgCRJcjqd6EqOW/DZz372/vvvT0tLGxoaQj4EpTjj4+N33XXXrl27srKyLl682N3d7fF40tLS8vPzCwsL4dUDS6FhgsfjKSoq+s53vmO32//85z+3trbGxsZmZGSgOUl2dvayZcvsdvsPf/hDh8PR399/8ODB1157DVlln8+3fPnyiIiIG2+8sbq6GplnSZIgC4FJZbVa7777brvdXl9fr9VqQY0I+vyGMkUcB086msmATYcdEP+F0Pv09DQ6FTgcjqmpKYfDMT4+DrmIubk5NB7q7++HTDPWf1QcQpbty1/+Mipx9+3b9/rrrzscjtTU1MzMTESjkOOF0VOPSQvheeDI6elpuIK4v3BdBC7DidmIEkm/3w8Z1snJyWPHjq1atcrv9584ceKFF17o6elBTPcb3/hGWloa9sGhoSGE87CHXtF4Xm+blyWAqHrb5XKdPHlybm4uPj6+rq4OXAOf3L4GePS3v/1tfX29JEkjIyNms/krX/nK/Px8d3f3L37xi+Hh4cHBwaSkpMcffxzhRbXcrBMLu8fjQbBSkiSI82B/yc3NXbFiRX5+vsViQZmN3++PiYkxmUxDQ0Nzc3NlZWU4YSAQp9P56quvvv3220CfBoPhscceW7p0aVdXF5LPIEFgp37yySchVJqamooGebQ60Z6O+jePx9Pc3Lxv37533nkHoltRUVExMTHIbZrNZr1en5qayofYLmecicB8/Pjxp59+enZ2FoUE09PTFovljjvuQEIPCp779u07efIkVE9iYmJiY2NBYnS73fAtBUE4f/78k08+iU0/Pj6+qKjoH/7hH7Chj4yMnDt3rr+/H+UKkPgrLy/3eDyQU8fi8Mwzz6AJ1datWw8cOODxeGZnZ/Py8iBs7XA49u3bd/r0aTwOu3fvRobf4XCkpaU98cQTHR0dTqcTIsvnz58fHR0Ftxwwcvny5Z///Odzc3MRVkCQDoOMKTcvq8EChc7Lmh+0egAJIPz3r//6r4cPH0Y0Kjo6+tFHH125ciURBBCjsVgsFoulvr4ePNKtW7fW1dWhRiIoHrs6W5ABoNe0E9ObBLVxbeiDg90LpXWxsbEIYCDqg9oAAALEPrEpxsXFIZuPBwn7AQVXgP8oqAmfD5IRgDs4BzzbxJBBQQzKsEBiBrpFT1AkTRBuAXQm3I9YJv0u8aFRoo5QpVZuDW00GiMjIwcHB7GqQsAH7jWKJnU6HcRPgf6JXwHEmZSUhKuGIuTAwEBaWhqTuUx8mB9DIS4UEfJzirDg//n9/vHxcaPRCCl3YAvANcYYQBj0QLBOzc3NIaaIHULk6np9Pp/T6Tx16tTbb7/d0dFhNBrT09OhvifIipaUoFAgeHqf/gy1glC8mZ9v9AJxemACIogjSMAWxubxFQwj6raRB8AkQTzG7/dTkRYFqilSjukBJlWg9wtTqVQgWcGbgrMHRzTo1cENwxJAWEHg+iqgEh2cJcaY2Wzu7+8fGxuDeKIoipGRkfHx8cCUTA4TUg4Xv4IZhakLT5VyHYxr1YwTxr1G4Q2U6eCEqGTVauIUIZQ+L0vUMcaANnDf0WZBFEXUmU1OTkLCDDMEPwoHAPtNfHz89PQ0ZBaRWJiYmIAKqt/vRyQYTC24puScY+bjMuEUIWsH0HngwAGE0xwOB1pxTU5ODg8Pz87OAty3tbWRmGxFRcWqVas2b97MGNNqtejThxSHRqMpKipyOByZmZkjIyNNTU319fXPPfcc5OQjIiL+5m/+pqamBnnhubm5hIQEhDAQi8V1odOZSqWCiiieyk2bNqFnH+YMaDNQsUAfOovFUlVVdeONN5pMJqQ3XS5XbGys0+ns6+sbGxuDQ+tyufLy8qqrqzdv3gzvCwEn6AhnZmaiifXMzExGRsb09PQvf/nLAwcOlJaWYh3YuXNneXm5KHNq+/r6ULxx7Ngxr9eLctiYmBhoes7Pzzudzr17987Pz0OQCu4TvPGVK1cKgtDV1dXZ2YkFH0XGDzzwAHpIYeoC2qKyYvXq1SgFRl4etYAbNmwAgQfUCCi5oRDQ5XKVlZXNzs7m5OSgyA9ZddRoCYKQm5u7ZMmSrq4uIG+koaiAG83XaWfFsKPkTJKkyMhIKDijbMB/hRXANCdpfYbXhFDx9PQ08lq1tbWAAsgwREREGAyGsbGxjIyMc+fOPfXUU5BsFgTB7XaDLA7cj6ihyWSKiYmBlJYoirm5ucgdTU5OAn9IkpSVlQUKOAWVAGSxLyChjQc5KirKbrcPDQ21tLQgP48lSCW3J6PMM3iAU1NTJpPJ5XKhlLmnpwelfdS+AKJhtbW1kiRptVqEsXG7iWnzyTG/nH6nXXVqaqqzs1Ov10M0RqfToV4IfZdxL/7zP//T6XRiB9mxY8fMzExnZ2d9ff3JkyeTkpKKioq2bNkC5g92Io9sBoMBDRmRTEPgHyn31NTU2tpatGSGFhmmpUqlgoQl/DSIXAuCMDU1dfDgQSxojDGI9fX19WVlZUFsFGuvJEkTExPHjh0bHh5GcbDBYEhKSvL5fPB7ca+xI0iS5Ha7T58+jbojxGqXLl1aXFy8du3avLw8dHrhCeuL7OC8oQLz+PHjb775JronoYuFSqWKjY0tLCxE+Ky1tfXtt99ubW3FIokzVKlUeCpBzccKc+7cOSyAKpXqb/7mbxISEoBCIyIikPHD/jU5Obly5cri4uKUlJTBwcGIiAiUHzgcjqGhoYSEhGXLlsXExBAmVKlUJpNpcHBw3759r776KgTHSktLy8rKiNra1dXV29sLes/w8HBCQgICiH6/f2JiAgmWmpoa1PGDkpCYmAhRLEBHlUqFIAgYoYt0ep6dnT179mxDQ8PY2BgI5Hl5eSUlJU6n02w2Q9R+eHj4pZdeMhqNJ0+eROjW5/OhzQhkA1WyRCz78DUAhAYkudCToguKGAMF57A8EdRARAekBQL0EXJzWaA6eEjYzgEr6WiobYVTBVCioBb4ZfIlf6p+WYIGFB1B7ggL+ALKAbInwED4OlZJOm1BjgRjNWSMAXfiJ4DDKFMxMTHh9/uRhAUOACYQRXFmZgaqncj4UxwR6WB8HecgyHqXSUlJUCMhxAO3QZCzyRgBQWZH8CsaUCCpTcFrxBqEmChjDARQbJMquVshwhWIeYyNjYGGhMz4448/fuTIEUmuN0BQipgnOrkzH001iWtZx2QiDd04/6XaVUgBrBuN3N+XQr+4FiEgVwCDI4p7ChDJ5DQCY8wndzMALoczgCQSBg1Bazy6iqkuyDJ5dGtIMgK8FIVnj3+JC8uDDHpKRVHEzES0gDGGfVclq4tgZoL24Jc17JlccUj0fZoS1F+CoD/9Ik4Mv4IBJP+WMYb1C/eLCmAEuYUhHFoMLx0EyQomC89hcQSx2y/T8IgUhEOB04LYiVeW9MGtKSwszM/Px5OFtIMgl4IxmbOLGcsYQ8QIInEtLS0ajcZmsxUVFTU2Nur1+qNHj7777rs9PT3o72a1WmNiYvLz82+88cbc3FwKl/b19b399tuxsbGjo6M5OTkPPvggkuBHjhx58cUXT58+jTVHr9fv3r0bBfpUzEMNgDSynrooitPT0y+//HJsbCz6l7nd7lWrVt11111VVVXIaQATezyeycnJw4cPQ5JCrVabzeZvfvObcCwxdak7xPnz5xGJAJ0GZOXc3NzJyUk84BcvXkxJSYmMjHz44YdNJhOIiwh5vPXWW1qttqGhoaSkZP369RaLZWpqCt2Ojh07Bj8EIRhJkpYsWZKVlbV58+Zz5879/Oc/n5+fz8zM7OvrczqdKSkp8A1Wrlx58803A6YnJCQMDw8j2atSqe6+++7Vq1cXFhbClZ2bm/vqV7/6wgsvqFSqzZs319XVWSwWQRAgGVRYWPjss89GRUXBV0QQh3Sr8AKPIXx+Juu4+2WhCMy6//W//tfk5KTT6UT7MKvVWlVVVVNTg+Jsei4w2/VyfxUmSy4yxnJzcxXP5mUaVgxsKJOTkygWfOmll6DBlZiYuHHjRowbPdoajeb2229/+umne3p6tFotOtbBLUR5D3ZP+EtVVVXLli0rKytD8BjamqgCrKysbG9v7+zs/Kd/+ic0i5BkFX86N8bV7CGIoFarHQ7Hfffdh1UiLi4OtafYIJD0w42gvcZqtX7uc5/7l3/5F1SgHj58GBpEOPKmTZseeOAB7AV4VDEaGG2QoKDdjiY52JuYzI3BzJc4semg44ztAwgB+lTYs3hmBYYO3f3UITo346HAYjs6OupyuX71q1/hbO+++27KDyNAhlKxY8eOkRrV//7f/zs1NRXtHcC8r6ysvOmmm5YvX47IFGL/aGaHnO2hQ4cuXrz44osvYo0dGRnB04HSQYSoaPRw+SAOIIeJe9HU1PTiiy/CJ9fr9d/97nfLysogrwnKNEra5ufnX3vttWeffRZUUpVKtWfPHqx1iOBQDA4Bsvb29ieeeOLChQtoIaJSqe655561a9eC8Y9gPAJhPk7gju7RIp4AENevf/1rsB9RjTkyMuLz+W688UaPx/PnP/95bGzspZdeQuS3qqoKPQSLi4u/+MUv4gGHEKcoim+88QZau4ii+OUvf/nGG29Uq9Uul8vr9R4/fryhoeHMmTM2m02j0cTFxd1xxx1RUVGjo6NpaWler/fixYtPPvmk2+2uqam5++67Y2NjkStG+F+n0w0ODr7xxhsoNIqMjMzIyLj99tsB4fr7+/v7+1tbW9977z2MMECdSqWamZkpKCjYvHnzihUrTCYTHB60TsdpQ6AM88Hj8aBizePxgPGImBpBKUSmdDpdU1PT7373O8Sq5ufnf/rTn6akpHi93oyMjMHBQeiW9vT04OuTk5MoZ62qqsrLyzOZTKBvUGBU8QQtso6Feu7+uz+I4j8U1AIWDNWJXK0w/3gTSPX5fETqBQClwDNYUDRfyXiUQ14sfyb0FMF4USTFv1KwlJYUoCiPH6L0Av9zWO/oi7zXIS5UuaFLQ+pHkJX7mcyKpowhP0SYPaLcfYPOgS6Q/wr+BR8aSyodysfJDSkMSQ8qisBN9Pl8Vqu1o6MjNTX1xIkTzz77LNQA7Xb7vffei6p5hJmZ3NAq6Oz5JJvCP/ZzYt6KG8F/HkYzRzFVJLmPID+pQrkoixvvzcIUObfAy6Ef4s//4zLFwyVdduiILUotJcO2hG0eGC4pKenChQsWi+XEiROnTp0SuFaAiLd97nOfW7duHdqa+v1+lBEjZdfZ2YnC94yMjP7+/lOnTr322msmk8ntdg8NDcXHx+/cuXPTpk1gnKM1Bz2Jgizmix5tw8PD//Ef/3Ho0KGBgQGdTmcwGACXq6ur4W8AeIE2dujQoQMHDkiSNDc3FxMT87Of/QzK31pZRBzuPU7s7Nmzfr9/9erVNTU1mzZtKigomJqaslqtGo3G6XQ++OCDc3NzRUVFS5YsIT9EFEUQbxAYe/TRR5cuXYqQJPKZlJ3wer15eXmbNm0qLy/Pzc01mUxxcXENDQ1wTrRabWFh4fbt2ysrK61Wa05ODuYnUjf5+fk333wzMrGbNm0CUp+cnETw5eabb161apVOpzObzRSlA47R6/UocdbITQaD3n16xJjsYvm5tkQYpZ/85CdYx/wyCxR3n6eg0C2TrpYRG9To9mFrb2pqOnDggN1uRyATdZzwb+HL6XS6kpKS2traDz74AJISFD6IjY01m80JCQkI+yUlJbndbrPZjPABhSfgA9TW1qLQsLi42C+3vgn14KCWURTFsbGxiIiItLQ0SI2Vl5dDWoC2aUEOcCBIB1HIG264ob+//+jRozMzMz/+8Y8Rn0pKSiouLt6zZ48mtEg8Qq2MMcwKSZIA1j1y60Ymp14FWeQ66HFw4UjBTU9PI92EXQmBeShdiqKIpFkoKhewvt1uR7gHFBSv15uammq1WsH5xjhgkM+ePfvSSy8dPXoUZJhf//rXTqfT7/fb7fbi4uIHHngAskh4WoERoXTidDqnpqaeeeaZlpaW3t5eq9Xa1tY2NzeHaq4bb7wxMTERtAgWkDBnMkBkjEGr4Oc//zkEH30+3+c///nKykpMKhA4nU6nSqXq7Oz8t3/7twMHDiQnJ/f09ICbV1hYmJiYiBAPkxW9GWOiKEKX7Pjx4wjroFKutrY2IyNDq9UiKUF+4OKPQKDBT9u0aZNare7o6Fi1atXJkyfT09N37txpMBh+/etfHzt2bGZmZnx8/P7776+pqfn+97+PMqRvf/vbERERKHafnp7u6OhoaGhoampCZm/NmjUbNmxAN+vh4eH333//yJEjHR0diHckJibecccdS5cuRZUtNACOHz8+Pj6+bNmyW265BRmeQ4cOIcFYUFDgcrmOHj364osvjoyMpKWllZSUFBQUZGZmiqI4Pj7+2GOPnThxgoi7SNL6fL6UlJT169evXbs2IyMDumeIZ4F6TQ3jUBiQnZ2tUqnwJsKOSMShFyT8dsRf+vv70bwFDthXvvIVJIFjYmIgITMxMdHU1PTGG28g0YoE44MPPlhWVoaGJ5j5LpcLk+fD239VGeIPBayhZxULK6VB+WeY/sQDTOBYkkUPsWzx6F+Si1wDcQyPvOk4QMYKAMQ4+UgesiuuReE5BMXWKq6XCn9ditf8ASVJQjCAovKS3BIL6Q7kg5gsoCmKIhwJGkaJc5xUcos+XKkok7AVtwpnywfdmQxtQ90UxY3TylrpPp8P5cgQFHc4HKAnRUdHb9u2LTMzExcicInjy8R2n0Djdz7+7uMFDxrYwmJlxVRhsq/FLpsouYjR3KYpGvSJYBw8Cjr/Py6TgnnXl2+XPHlaAZA6kySppKSks7MTZHpkGxDPM5vN6BNSV1eXmZlJUSW41gkJCQjNAli0tLQ0Nzdj4UZbn4yMjOTk5IKCgsrKSvBqKA7k5zrxYX33er0ejwc63+j+6/f7KysrV6xYYTQaERVWqVSIFTE5haLVamtra7OysqKjo8GuYTIqwmf0ev3o6Ojc3JzZbK6pqdmwYUN+fr5KpaIm5ZIkbdmyZWpqKjc31+/3o9xCEARgrLS0tP7+/ry8vMLCQnQXoXODhPbIyMi6detycnJqa2vRB2Bubi47O3v79u1vvfVWQkJCTU1Nbm5ueXm52WwWRRG1KATZ09LS1q9fD+INAleIOEpyshfNfeFpIOCH3LogCBSppXA1v5zSTOZXaeJQMY6ohvgZ8RKJ98LX20lyWpLJ+9GHN8SzabWfm5sbGRmBEl9KSsrq1auXLVvG5Iwik3FwUVERMEpnZycqRnJzc4Gns7Oz4QQiuWS1WvFDyPLRFon7GxkZCbY9cCQleAONZGGnp6dTUlIyMjI2bNiwbt06pKyRqaPBx34EyhAyBnNzc5WVlcPDw8eOHdNqtUNDQyaTqaCgAHQ1pCzYQgiLAYdXjLAXRA5QSo6cHjxPwCAmpxBDGWoSZmdnu7u7kWtFBhscASY7LQaDwW63I7IeaIh8I7k6MjLS0NAwODiYnp4OOgAK5zBnZmdn0coNX3G5XEajcXJy0m63JyUllZWV5efn5+TkJCcnQ4IcZVoqlQo+T0tLy9DQ0IEDB9A+pbCwsK2tDQ9IampqTU0NSDv8us0WbiVYK/x+f2dnZ0tLi1arhbgfQgnR0dGAlWiW0tTUdOHChQsXLkA0BmTjL33pSykpKVgEaGLA29Tr9Wh8BuSamJi4fPny5cuX5+TkCDINDAujRtYtvIKngjGkrGtqamZnZ7FWOJ1OYAyHw3Hu3DmIIK1evXrVqlVwurKzswEIMzIyXC7Xm2+++fvf/76srKy9vR3RhOLi4oqKCtS7z87O/u53v2toaBgeHl67dm1zczNxOqDtGxsbW19ff+jQoZ6enqqqqtWrV1dWVo6Ojmo0GrRk1mg0paWlJ0+ebGxsdDqdMTEx0dHR69evz87OVqvVSFa0tbVhI1ar1dCkLi0tjYuLq6ioqK6uNplMExMTSMeB4wrFDpQ+19fXv//++xMTEw8++GBhYSGxM2jRw7RHzAJFYmgyAInqysrKkpKSlJSU5uZmVHdERUVNTEx0dnb29/eDUhUREVFcXFxSUoJnEK41dr0rulmLmDoQ/fNTQYEypWCBT0GmiRM84hdxiVPkZQtVXIKeELIqktyDln6FFnQFdOAjsovDfTpbcSFfguA770gsYpKcCqAD8huPT25ugG0eaRA+us97ONgyyXkQ5OIBSn3wZ87kjDbIP/hfrCDCwm7N/GgDDKlkE+SaY0mSINv3xz/+EUSpbdu21dXVJSQkwHtmXIOwRSI3Hy8MDWWKuy9xHYt5/MG/4C9E8V3+yOTX8b/FropdoPjRxUeSzw/wUyLUdP0I7gvvAwiXF3ZVgL9Ab4eM1jiERdVqNeQgzp07Z7PZoPybkZFRXV29bNmyiooKBMsBdEggFQxXn89XUFBw9uxZFMFDu02lUqEo8M477wQlF81xSJeDniZMHsiJIp/b3d09NDTk8Xiio6NvuOGGFStWoOgZByGX3mazqVSq1NRUQRBuvvnm5cuXu1wuSvfRKodB+D//5/+gpTrYGtgRsRp4PB6TyeTxeCwWC0CDKGsnMMbi4+MfeOAB1Djq9Xq0ZwFk9Hq9ZrP5xz/+MYgler0+ISEBm6tKpcrKyrr77rs3btyIggrweYAJ1LL+Eu6CwWDIysoCuYLPalJYWqfTgV+EhIwg58owFHyIhM+d8lOCpoF/oZStJAeSjEajX+a7Y53kaWlBD3UNDeFY9Cryer1xcXG1tbW5ubnV1dUWi4WCMhgHaHTU1NQUFxfDgZmYmADfIyEhAb4rLo3q0PAxCrSDdARPD6xUCMBPT0+H2vuTk5Pz8/PLysoMBsOOHTugHMVkHo4gp8uIs8dkPA23wWg0Qoz78OHDGo0mMjLSarV++9vfxl0DGSzU4Ehyq024vvgtoFJRVkiU5A7loag7cHRVKtUvf/nL5557TqVS1dbW7tmzJyUlBd0eGGMQkid54qAGXCFJEmpqjx075vP5UlNTV69eLYoi6mH0ej2q3p1O5wcffNDS0kJBN9QsFRUVPfzww+B1OJ1OTGYUcJ85cwYNEyChU1FRsX79+szMzJdeegksU6/Xu2HDBvj5ULdkwWYmtCBxL373u9+B5jQ/P49CcEmSBgYGIELt8/kaGhqefPLJsbExm80WExMzMDAQExMTExOzY8cOxlXoAcPQqb7xxhuA/sPDw5/5zGfuuusuMI5QrAXqHZMryBeZ/EENS4rVat25c+eGDRsee+yx3Nzc6enpX//611hJ1q5dm5aWtm7dOqPR+Oabb2LirVu3Lj4+/syZMz/84Q+hn5aSknL8+HGQc6xW6/T09G9+85uzZ88CKCOdgiZfwN8ul+udd9555513RkdHIyIi4uPjb7311ltvvRVycH6/f2JiwuPxDA8P+/3+t99+e//+/Qixr169OikpqaqqClIupaWlyNhoNJqpqSm4hdHR0d/85jcROUKKFcQ8SZKgTTw2Ntba2nrmzJn6+vqenh5wuYeHh/Py8oCvQOhSq9WogsODrNFoUMf8zjvvwLfftm3bbbfdhtpr6GSoVKqLFy/u37//+eefHxgYgF9dUlJy3333paeno1MBmrdKknQNy2+UFCB6QcuEGIxkHBRe4x4jBA4PHpEGBVBQBJV55IrlhjgntGHw7gRvLAAlE6Tgjx/0Azwi57elwEuj9IWfk+wEGYtOjwaHhBH4D+OieM+BD8YgtY3j0DPMU0T4Gwb6suJQ+Bj/i3SNWNqA+zFpsJNNTEw0NzcfP34chRPIoEGkFlMZGwb5daEW7k+40X1UwA4aK8X7ZAqHkP6l+cz/xFVgDuJrKn5I4ExxPoEX9Sky/oqEywg4SZKE6QrkLQjC6tWrLRYL4n+MMa1Wm5ycHBsbC0QI9rMoilSAJQgCKu2ioqJ27twJ6d7ly5c3NjaOj4/v2bPHbDaDvS3JnD3qGSdwEmRMLgFCAbHf7x8fHxdFccOGDbfffrvFYlmyZAnVojC5HHx8fNxsNq9bt668vJwxhopnSF7w4QMmT63MzEwiTzP5MceF85LPoB8gDE+stnXr1lG1Bhggoigi5uTz+fLy8igv4ff70dcCaF6n0+Xk5DA5aggJPC3XMQOvgexhPllEAROYglLYgOku+2TFNp+sS0uXwwK8ZcV85vcFfvLwizBwM+NCVPzKz0I7xldqGDSkQ1EAgA7xX/jCF7KyskDzxWdACwZ/HZXoiNQAWaakpPjl6ltCYBhebCJUtEOXo9PpUOoDU8uCXUHP0+12L1u2DLQQFCjj7tCNoNonvyyzgRsKRo3b7X7qqacOHz4MXSydTvejH/3IbDZjtkTIbbkD1yUkfMhTpR3c7XYzxjAITG4EBIwb9PxBc3/jjTf++Mc/4plFlbnT6TSZTH19fb/4xS8YYy0tLfHx8cuXL1/kfuGUZmZmIB0jimJbW9u5c+f0ej08cPhUuKd+v9/pdCIZPjc3t3Llyrq6uuXLlwuCgOQMMOXLL7/87rvvohsXuCs33nhjVlbWmjVrGGN2ux0VSh6PJzk5uba2ViM3R8dZSVySme4g7ju0KRljarV68+bNN998M9R7sf92dHScPn36D3/4Q3t7O37aYDDk5OTo9fo9e/aQNpSKk3gC6Jqamurq6kpMTITIKfiNk5OTAiey7Je1a6WF2qmXY1gJoZGAsILH4+nt7QV5bPPmzffdd19ERERUVNSrr75aX1/v9/v1er3BYHj88ce7u7u/8IUvbN261eVydXd3//a3v8Wi/dRTT4HaB1WPlJSUtLS0J598Mjo6+r333vvBD37g9XpRfOVyuRD4KCkpgVOt1WrHxsYg1AEmniRJr776KgKaNTU1f/u3f6vT6bKyspDGsdlseXl5zz77bH9//1NPPXXixAlE6/fs2bN+/foVK1YgS4AMwOzsbFtbm81mg44c5g/8n9tvvz0/Px8KK6S1jYcLBcGY/3/84x+PHDnS2dmZmpoaHR2NCiufz9fR0WEymVpaWp577rmurq7R0dGRkZGcnBwsaytXroQILJZr9MCJi4ubnJy8UjnjUKbmFxSFG0CwUrGw8lNZ4IT8kR/H4k5BEf6AQP+AuT5Zk5GgmMSFtBm3AfA4iQVsDwpkTysUf4Y4BwWuohc8euZHQOD8hMD8AGKQSJ8R8Y4xBtkZpMKZHCrDgss4MjrR71D8SnwAoG3E4RTonwes+IzAsapoaxQ4/weAFeifarbwycHBwYMHDzocDmz2dXV1RUVF1CgHezygzCIEUBYajF6rDfiqjZ8G/H3nMb3EUdqCThuJy+3Qa34eCh+OcExzUlpYiLz4RV3mka/ulC7fhGBRgEt+hXHFFYF4ggwDgmcHLqtGo0GXRMTwsH+T+AaUCfDgCLIkAFhtWq1269atk5OTFoslPT09MzNzeno6KysrKytLEAQEn4jG7ZMbvqrk6nDcFzRikyRpeno6Ojpao9GYzebs7GzEqAB9wHnAeo0gJTZs9Dax2+0ojfVzFa40Gtj5QBVVy+pVCBsjMIniS7S9RNCdH67x8XG4puiwCzE0URSh1kL0D4B+XC8WKOzcWq0WBBuCibToUVETFhBIAuC/sJhjn8PHCIvAZ8D/4gxppSKiI3+v6bUi+UmGqyasSZ9HLRk/wWDXsGxJkiRapTUaTX5+/t13371kyZLo6GiE8EGzIfkECn4RLRaIHBgIUxpZVqhHYFgwc+hOgfcFpwKMGmw0oQA0UUwxr1AECflaDBflojG7xsfHfT5fbGwsJtLQ0BD6187OzpaUlEAkURCEiYmJ2NhYqFKyYIVSosxuBaSOjo6GPhhjbG5uDjwQuDqLQ0yInV+8eBF7ltfrxeMpimJvb+8HH3zw7rvvggienp6+yJKLJxGnNDg4COXx7u5uSZKQRoCiAHJlarUaqi8Yea/XCxVaURThDzc3N/f29o6Ojh45cqSvry8hIWFoaCgqKqqiomLFihVZWVlardZoNKrV6ubmZmht7dy5EwrxVBTODxcZHrGhoaHXXnsNg6bT6Xbv3m02mwcGBhISEsBRfP7555uamoaGhuAN4i54PB60vITApWphp2qKuqJrrMfjyc7Otlgsk5OTgBnotMjvg1chkAUEgqdsZGRkxYoVhw8fTkhI0Gg0ycnJ1dXV8ASA9CwWy8WLF20228svvyyKol6vLyoq8vv9FosFgXyn04lSKPjD6enps7Ozt956a0VFBTIYGRkZBQUF58+fxxJaWlqamZlZVVWFXgozMzOkgKLRaJAhmZ+fHx4ettlsycnJZWVlycnJfrnqElRMZFwlScrKympoaHC73RiivXv3vvTSS/iMSlY35gEeZpfZbC4tLb3hhhvMZjOtQqBOoFMTnkEssA0NDZ2dnYIgjI6OIivCGEMR13vvvXfw4EGbzQZJCZSpjI2NJScnAy7iKdbI2vdXdJsuaUEmKD3YCsjLgxX6JI/sefBEMXUetuK7Pq6nOguI0Qpy7I3fTgg38yej+BYLjfPop/nPCLJTIcgRen5c6DOknEiQGv+FFZmukd4XuVa+jEM8NJ7CwkiAX9bt4s85aGED/sQG4OdKWrGn8jEA/jZhwLHcIJgKaeqf//znUB4sLy+vqKiorKzE5oQckyi3B0bU8xMo93aZxs+HwP/i7w69GbgU0p2lORD0gFdk/Myhd4SFxcqBZ/4hf/QaWuBKJHAu0+V8/pLHp4cOzyAaYKH4Cf8LAgNCBgCgWJ0JxY6PjwNJG43G2267DXGU+Ph4NAijbQPwDvJqKJpn8jpG/r9fNqvV+tBDD+l0uoyMDITt8ZiQOgoC8JBzFQQBYEir1QIFAhPQWkq3m2oPEBvGcKGqGEFlUpjBpUGdXZIkJJopZQcA5JdF1QQ5ioGeR4jR0OUwxiAjiGNibQF1Fe4BTziZl5tXMMYQLsVerlarEc3iER7RdUj8gHdxFWk3fs6oVCqgMYELrGAQcJfpusSFqhKBK/81MfwWIDtSMcnJycnJyZBMhUYtys2Jio2sCLK4Pp8PBEsMKe0y+DBo4rQt4taLcrcBWtiTk5NxgwATg54nKDRauYOK1+uFojlAmCSTzeB+MMZAqcIG19LS8swzz5w/fz4lJUWn0z344INLliyB1k1aWtr8/LzRaEQwiAU85nSXRVEErOno6EC6w2g04leYvC2iEUTQ8/d6ve+///6+ffscDkdiYiKacEdHRx88ePCZZ57p7u6OioqCWA31RAt6HDxBbrfbZrNB/yoyMjIqKgqPHiTwwWTDE3TDDTcMDg4eP348MzNzbGxs//79R44cQctOs9nscDgYY1qtdnx8HG7znj17TCZTVVWVxWJhjPX29nq93ra2NqCaqKio1atX4y7jDi6iVjQ3N3f69OmXX34Z0xtd1TCf0bX31VdfbW9vdzgcCQkJW7ZsGR4eJjQCpR3/QhU72pgw1DExMd3d3fBP4LdAD530sqOiopC1EANqIC/HUPDAGEtMTLz33ntrampAW7dYLGazGQtRRETEqlWrTCbTT3/60/j4+MjIyJ07d65Zs6a8vLy7uzsuLg7JtKamJqgJ6fX6ysrKm2++OTc3F4GJyclJtBD51re+Zbfb0eYMrhcapEiShOgPslWk0Or1ei0WCyia1dXVqN6Gm415OD09DVn9mpqawcHB06dPm0wmOMaIuaAWWRRFJAdQf5Kenr5hw4aCgoKkpKTMzMyBgQGg/wi5FTR1VRMEAY1BxsfHUVXl8Xg++9nP7tixAx0S9u/f73a7W1pa1Gq13W5PSUlBd+Tjx48jnv7BBx/ceeedyL+hacD4+HhmZiayu1d6v4KacnbycESSozV4B+t40LVbkiQAR4kjetKfPNIiQjx2L5q1ChyPvZ+HXIplnQe7/MlIAc5J0M2AvxaFr6LwE0SZQE+fwX/xRcC0Y9H5E43ML/flVskK8QokBMYUsYaY3NmRIj28AwMHgE6b0E+ouyvJTFycABhpNputpaWlq6srMjIyOTn5q1/9KkiHKpVqdHQUVArkphHTQrAK0/rTazyw5h2kwAQXW0hLYwvLc4OyF4K6DYubSm6ARfFmmjAf/mI/AhMWtnamcVgEe12RD0B8CSBL7DRYtQW5Aze28HlZgRTI2+12oxtJVFRUbGwsqPmo84PCL8QKqbufKNf7QluNELNKFu0V5YZxCK1ptVrUVjLGAH2AlZlM/kFCAEWKIEKARIFkoCLDSVk7kvpVyc0ZEK0npwIQxCN3tVPLvaURdiXFXhRf4lokSQIJCugTAAjPMom0YGXwytqvkkxMhyAjj7axUgHWwFPy+/3Ya9WyKJlfljAGEsX0pgdHkqMbfMBFsUpLcgqU1me/3O+Fyatx4FwKXPav1OEMZbi/mA9wfsBmwXaO1RLtI7FmCnIgEIKAKpUKio2CXHkpycXEKpUK6SyMHsX+EXsGFZPwGcaBmnUEGiKj8IFpbNG9hCj4mM8+nw8E6/j4eBBBDxw4cOrUKSCkf/qnf1q+fDnuPpq1wTMkx1Xxu0hKUAjZbrd/5zvfQdz9S1/60h133EGi4WCAhFrfGhsbf//7309PT6empoqiuGHDhgsXLjidzqeeeio5ORmV0ImJicXFxVarFWAr6HGgAarX6wsKCm677bbu7u62trb+/v6cnJypqamUlJSlS5dmZmZCATMrKwudMR9++GFUUUNQH9U74+PjqIQxGAzV1dW7du2C3gC80MnJSRB+nE5nW1tbbGzs2NhYenp6Tk4OEv6gcYeah5hUw8PDiDtUVVWtW7cuOTnZZrO98sorkI7FAS0Wy/333x8dHX3s2LHXX3/daDRu3bp1YmKiuLgY0sm0gAhyaYff709NTUUDgcjIyDNnzhw8eDAnJwcBEYo04cGEUyGGoMKGMsxkhFFQm1tZWelwOADiId8MnGq1WiMjI5944on5+Xmr1Yrely6XKz09Hf+7bt262267TZIklGhDGIeUduLi4tDvKD8/f/ny5ciRIm82NTWFpBBKtIF2NBrNsmXL2tra9Hr9tm3bbrrpJvTZgNyWVqsFDUwQBGSEwLwqLS396U9/2tnZiVwrHiWo6IL4h8LutLQ0VGCjcsDr9aalpYFwhacVGwoiAmjyYzQabTYb/ATG2B/+8Ifnn38eC+zMzIzb7Y6Li4uNjb3tttuWL1+ekJBgs9nOnTsH0N/Z2fnDH/5wx44dWq32yJEjWPY3bdoEhtIV3a9Q9t9PdSAIxigIckgeX4DzRKsMzxUhCOWXu1ZhEKWFsR8kMqjYl8dhNC/pZMhhEDmuJ+9UgAnAg3ge+vOwjzaeeblnnppr16UYB3puQRFWHJbJWIf2bzorPpbvl0k+jKO90qH8cn8D+iRFs4jeKnCBfD7ThzcRfcG185ruTOZWCXJT59jYWIfDAdSSnJz8xBNPiKI4NzdXV1eHjRxAAd00GRe9gG9D+WUWwqEKtGu1AQsLEyaXAzSDWuACR7dbWljVzQ87XQX/Af4gBOZCJbh9XEdn2sj9cimeoriFAESgm0HXy7sri1io8VEcmT52pRxQxfnQm4ucGO9QBT0Z3vjnncliLPOyHr/X66VYJvATwU3sQIx7iKCJgUIrHA1oW1xYbsQYo0aE/OVIsteNfyVJio2NpTlJQjTE4KcPM1m2hcoSRE43mSYYP2KkHMDkjg1EDcfDiyOr5JaT8A0IFOK64BQJclYWf9JxNHLDDRp8QE/6Xfokj/kkmVZBxn+YcT4w6TLzD4XEce0krmEIC5jPgNr0Rd9C7aDAuRRo12rlgdE40GnDF6WMx/z8PJAEPdEYZIwzkwcEayyOSaRKTFQKAdBBsOng6zQUi/B/mHw76EcxT5BKgiAsTSH4vbgWl8vV19fX09OjUqkiIyPRkQAAF/6JWq3mReL5YacbJwgCxV+7urocDofNZhsZGbHZbPAGib0ARhAGEAUnarUaubLGxsaLFy/CRV+7dq3JZILGfFFRkV6vb25uTkxMbG5u/trXvqZWqxe5+0AmWGxvueUWh8MxOjqKBo6g0IAvDodNrVabTCabzXbPPfccOXLk1KlTNpsNrB7AO4vFkpOTU1VVVVFRYTabVSqV2+02mUxoyAXgOzAw0NXVBfHQgoIC8sxRKhAqU6FWqyMjI+vr65E2iY6O1ul0Z8+ePXv27MmTJ91uN+5XZmbm9u3bCwoKELpmjKHTCIRoMH8Q+wDohFgWcFdxcXFvb29XV1dMTIzdbm9ra8vIyMB0AouPMTY+Po5gQaj1P9RQU02IwWCAJ4xIBJP7xtAEhnwnoAUeZKqVYozNzc0h/sjkHpQ45rws7gKPjtZwxOP5JxGHQlkUerNs3LjRYDCkpKSsXbvWYrFg7qEHGVxigVNpx1MZHR1933332Wy2I0eOwMdgjKF1fUpKislkSkpKQpaPAkNQ5MSDxl8R8BL+xcG1Wi14dA6HA4FUo9EIwg8Ofuedd65evRoZQvCxX3nlFahRnT59enh4GKfa3t7u8Xjq6uooU8S49ZnJOCTwli2yL6sD8YTi+/wWpQDWUrCsLn1d4UXQ0SQ5DsSCLSj0FQLcgpwQCLxaxrFlKAYvyLVigdelgOx0bvzGzBbuSfMBLZ35/6XhpjfRCZiMcTBCMfSKnws6eoGvFcsf/ytsIaKiazQajaOjo1i2ent733rrrd7e3vn5+aqqqm3btlVVVTHGZmdn0VQolLxa2GCKe3o5aINmMv/ntYUpl28f1++GLWxh++gNvYSQFwIyBqgyGo09PT29vb3/7//9P5fL5XQ6t27devfddyO6x8OIxUMt8I4Ap8bHx10uF1BIZWUlWNd+uYRarVbDWcWflMQzGo2dnZ379u0D77moqGjTpk1/+MMfmpubwaj52c9+JorixMSEyWTKysryX6pWirZ4IDaz2QzvCyEDkSuWg3eUmppqNpurq6vhKgiCgDgxCIfoN48ObsjbMMbQCh2/1djY2NDQoFard+/evWnTJniDSIUhuBbqPME/hGTN7OzsqVOnUEszOjqq1+urqqp27dq1cePGpKQkqEhVV1fX19dD9n7FihW1tbV8YI4igwhxxsfHV1RUoMtnX1/f8ePHAR9/+MMfInUARanY2FgmNxe6rsYjInpHkqnRjMNX/If5ryy+c4GnIElSYmLi5z73OUTNo6OjUfesOBqPEgGmDQZDZmYmOrVTylGlUqE4SqPRwE1FhEWQhT4XiZqhVAx1PllZWf/8z/9cX1//i1/8AnMemY3du3dD2i4zMxNOFHSNJiYm+vr63n///ZGRkaSkJJvNZrfbjUZjVFRUVlaWyWRaJPwf9NFYZOiCVBUo7oTiuHz0nY4eeF/pu0Sx4DMMfk6vhr5IBBieGiEsjPfzTgXvAPgDlOYCj08OgMBlGCjgpIDO9JoPuvBzMdR18U4I/a5/YXkuvaBR5ccwKJTnRylw0CSuxsAvcy5xzPHxcWSB0TD1xIkTx48fHxsbQ3fDvLw8+LtYkfkOc5cPcP8KTTEsi4wSPQV+rpr8qkeVnyRXd5DwDf0wxj+wi6OQsIXtk2BgyyjiXHNzc729vdHR0d/97ndBNzKZTI8++iiIW2CJ0Ja6+IoxMzMjyhXhycnJ0dHR999/P/a+qqoqSikjWyUIAupwEP5njPl8vtHR0SeeeII6pt1yyy0XL1586623SktL169f7/V6+/r6UISwceNGVEYuclYiV2uHbANQGqRvBTnqR2OC64WALyL62GGhNEBIC24MYr1EiAV87O7u7u/vNxqNN95444oVK8CFRqHtIpROnFVOTo7dbh8fH2eMgQskCMLu3btXrFiRkZGRkZERExODJH9UVFRNTQ2wI0hllO2hQLsoipQgnZiYWLVqVXFx8f79+/fu3YsuYwaD4Tvf+Q4kiSsqKubn50FkQhbiupoCJdKboRwAHvUpUFzQ4/u5FuOoJ5YkCTedZ/BSYlkld39Cvgv4JyYmBvrLKlmP0efzeTye2dlZNBYQZVkzQY7/hrpe3Ah83ul0xsfHb9++PT8/v7u7G0Syubm5uLg4tKpEGhn3FE5vXFzcypUriSFmMplqampiY2NNJhM8BCR/eEQqLey1p/g31Lgt8CQIVdPNkLjULUYZPjTe5FG4ooiW3ynZwhyun+uYwCN7HtYrLkBh/BLAL1IKP4GfXvTTAldcSAIXQWcn/1uKNxWOBONQPl+pTe445X/5G6b4FcUt4MlLgesd/3layxQ+D/5EzVN0dDQErU6cONHe3h4fH79ly5a0tDTIZoH0T1orQX8lDBwD7XIWJjwa/CcXH8nLH+dAF/dyLHwfr84C7294JK+HhUf12hp0CcGj8Pv9QIEREREajaa1tZWEXO69914qJQ/c8hYhiBP3mjEGpFJXVwd2BMgqTM5aU0UZzgr1Mzqdrqurq6WlxeVyJSYmVlZWvvDCC2+88caXvvSlTZs2JSQkfO9730tISHA4HIWFhdu2bQO9SrGt8+ZfKKqBhAOhIkkuSqRKEnwGGQy/34+Dq1SqqakpYgPiWyDtEDUF9TyiKKanp5eVlVmtVnSEBQkHVUmL1ABg6CorK+12+9DQkCRJEJXPy8tbuXJlTk4O0hf4enR0NKhotbW1gLYWi4UokcCOxEzTarWoewa4r6uri4uLe+655xobG/Hm/v37GxsbRVEsKytDURBfc3iZFuq6Qt0XBaWTBQOv/Ps8xeVy9lmoSOEF6mVJOIu+LnLaRz65vYwgl+CDZKWWGzHhBIi2B/dPWEgbWcTBg3SKw+GwWq1U57l06VLko5BQQokXenjjzMEc02g0BQUFEG4SRdHtdouimJKSgoYw8/PzfBEwj3jZQujOj3lIByBw6HnjR5+unLC7xJHsA78Y9Pf++4fVaoFjvROQnefULelHFW4N45YkH9cFg64zVPicya3m6TP0yVDOHMlxKryjUMYPiF+uD1ZkLRVTf/GBCjwfhUtGiT+2cKUmx8DtdkMN8J133rHZbNHR0XfeeWdmZiaENaBszRjDehFU9HORDSBsixvvIgZ6zmELW9jCdv0MKrfAH+BIqFSqkZGRxx9//NChQ1arVRRFcEE9Hg+AO8+QwUEW2acoAT4vtyuOiYmBLiqF2HggAsYFWl9LktTd3X3y5Ek4HqDX/+Y3v1m+fPnnPvc5xpjdbj9//jwwbl1dXWlpKcLhi3REBp2dNBUEWRAWuJktJBFg90TgXCFPBJEACveKshIgJAdQHIwv1tXVpaSkmM1maGGhehtXPT4+HqpnGcoDNm3aVF5efvvtt0PpC52/oqOjoSIKSV84MKIoTk1NGQyGiooK9C3GKSG1ArlYFDSjY/fQ0FB6erperx8YGCguLn700Ufffvvtffv2CYLQ1dXV19cHjQSz2QwVTtD3r58pIIQiTBk4u/jPX078EXUCuNcQeCVkzx+fUCWRfEiWFzOfYC1fvEdwjkfbi58VmrXDf4BkAoT8U1NTiYxHjT7g/uEdzEm1Wm00GtEOHLNCq9WimB5VuAomjsINuHxTK4aGhUDzPAjmyQzznLp84Nfx+PHLBz9qEld8GTisiimiOB/+mskBwP+GQtswkasVVrgNijOhz/NOCP2XoiyBLawHoFPC0kaAeMnprwAAE9FJREFUT+HOsoCpGejhKC5BQQFSXKnASWcIcnhDr9dPTk4+/fTTR48eRY5169atqN3Bh0m8lopvAn/3kk/gX4ktMrWCGt0UiRMbXfzzl/nrNKMuecywfXgLHO3wyF8PCzWqYYf5qk2Qu0CC0zIzM/Piiy+2t7cnJydHRkZ+//vft1qtUIiy2+1o+gsIInDxzlAHh04iBcgFWVwFaEyStbzxYcSb3G63RqNB57gjR468/PLLAMqDg4Mvv/zy/fffv3PnTsSh/vznP6ML2LJly7Zu3QqZL9+iHR5QOs9TiAHyAKMlrl4RH6MMBqmSIGBnNBrxAu4EhJsIscDNwK+kpaUlJiZCtxGD7PP5ZmdnSRk2qKFEGJzvmJgYnU6HnAPJqqKqlTS10K1WEASi+ohyZw8EiQ0GA3aZmZmZiYkJxlhDQ8PU1NTU1FRra+uJEydQYy0IwsTERGpqakJCQmJiIranuLi4ULKSoW79lT6nCqoPCwB4fOiawmRBPx/q+BgfzBxSMKMyDMU0Rp0ukj8qWWueheh7K0kStNfwMXIPFjE8PvDlkN1Sq9UWi6W7uzspKQlV/iiyRx08nAG13M9RkiRoB0uSBBkuGDI8mBuBP0rIkx/JxVdONV0V4+CpwPFJFHCTsh58+iOoz0A3RlrIaaG7xYLBKULkCpK9wsUhSB308+T5KVA1jFYHPs9Ah13kT35AQl2XAiLQn4E3jL5On+HPVuEB02tekIG/a3xOk8meK5Mr64eGhvbu3YuG2LW1tZIkTU1NSZKUkJCA+jDEGHid5qBDFzaFA8YuNUSB5S4UdrqKXw9cFq/UQn0xfKMvxwLDE2EL2yfZoEIzOTkZExODJMC5c+f27ds3MTExPz9fVFRkMpkgiO7z+UwmE01vYjj4Fy26pc0XqAiIGRWQKq41EG+IgIIkMzw8DH7s8PCw0WhMTU3dsWOH0WicnZ0dHh7et2+fXq9PS0vbsGFDenq6IAjUmjrU+SCaywfvsdjyGzfFKwkhSVx7PvwXJFmIDUJgAziMlHOx7VJGgnZnv9xkI1SmAsKmIKkD9kVGRrrdbiRhmFxdDX1Vl8sFOSZ6H4Qf1CQ4HI733nsP6sbI4Y+Pjw8NDfX19Y2Pj4+Pj/f398O7gFIQpDNjY2NBa0FDD4XG1zU3BdCiNxffyAjm8QcJam63GxiaMeaTW2rwM5D/OgXjABR5NI+aFmTMgv6cKHdiXfzM4bN5vd6YmBiz2czktl+ZmZmMMTQIE0UxNjYW3imeU/iiyP/AmYFiFSYeupuhg0SgLo7A0X74AWQBzg9vasXQKNyywGORihxVUQiCAH1o/oR4B4AtjMrjaQS1hodHBOWlheJCMDoHiSv5ZRxFhzA9jFf2ZAvhl0/uwovbzP8cWwj6pQDFOjLFeQpc6J0t7HD8XwO9sIub4lcCz5Puq8KNI9UjOpOg40bvj42Nzc/PHz16NCMjo6am5s4779RoNAg5gP0vSRKahlA7grAtYnQjLtPDVjgAEpd3/gjONmxhC9tfs2G10el0ULY5efJka2ur0+lUqVRGo/G73/2uwWBAo6i0tDSUIWJbJweAwHHQ4yMYSWsagAFUzIFRsNsSjJ6bmzMajaIoulyu8+fPHz16lDEGPfWxsbGf/exnbrfbYrHY7fbvfve7MTExbW1tt91229KlSwGAqDlGqPMhCjGvxy1JEmjuOBmqRgDEp+IHaWHTTMoJoBkCUDL2zaioKNLdRvcrNN9AogCjTQn2UOeJgyOTMDMzg65/c3Nzfr8fhCUEhqFEBLYPn4UAwpmbm/vggw8ef/zxqampubm5yMjI+Ph4RPqRFvD7/ZDnj42N9fv9MTExd95558qVK8vKypicMLkknP3wFgh+At9nAehR4QAsYshuAT0LgoAqbeRYWADQwjwM9O7431VEkOG10qRS8EECbX5+Hr1Z4GWBDiSKIvVRMZlMHo8Hkwd5OaTdIDqECxdFkXrdgFMEF1EI3ek86Fkt4gAImMdsoYsmyew9flzw8IClJMl6O5Jc54qSC/Kz/ZwsT9Afxg3As8eDXbjXfJKOcWAr8DwV6p/0moAXD78WuWf+hUXJhOAJEyumJt/6QOQ4jniWFFNcCF3trsii8I6K4pkh48+TDujjOpGJXAGNKIo2m83hcGi12vj4eDSqYMEyDJJcz82COVr8ZKB/2aJS7h+LhZroi9x3/ouBX+cvny10AFiAw3b5vxvKQh0nbGELW9hCGQWegDOoGTBjDMAXweD77rtPr9c7HI66urp169atWrUKoWgs+woRiOthwDooln3kkUcaGxsnJibUavWXv/xlVA9DOOjixYt33XWX2+0uKir68Y9/DAF7jUaDVnfXG61+igwh4ZaWlr//+78/d+7c0qVL0YSO6CUgoGdmZpaWlmZnZ5eUlERERKB1iU/uPceu/74TCidcaSDsWt36a7VfL3L8xfHe5R/nij4vyn2NgmLFQAupJ8p3eCFYyfOF+P8V5S45MIHT2Qw1oFQEzBbCzVDno7gSYWFdAV745VaLVzGbKVsUGNRf5HwY55wQgF7kxvPnvIg3sojxTg69KXEMKLZwEiPZmpSUhEaMuFN+v59v7c4fjT9PvK/wnumdTxr0D1vYwha2v06j9ppzc3N4jZgxioAnJiZGRkYOHjwYERFht9sLCgp27NiRlZUVExMjyTI4oTgP19YgP48XlBnQarUVFRVonAz2dnt7O8pka2trLRYLWPiIjOJ12AeAIQOTlpZ2zz33tLa2Tk9Pt7a2qtVqs9lsMBhiY2Pj4+MTExPR0CouLg7CMvjuJdFh2D51xrtzPMALicNZABglwKfAweRSiDL7n48KU0rrMukN/HIjybwIMAjpM4Fx9MDJGoiD6fiXPAfeiO9IOQdR7pgd6vP8n6HixIFneEVnFdQUQWi6C4GnAYuIiIiMjIS+G7SW+W+Rz4ajEWeRnz1Br5T/3U+78VNx8QVR4f1e39MKW9jCFrbLMCB42prRitjn86GYVRTF559//t1333U6nUuWLLn77rsrKyvR6B3xIHDfFf1wroehJo0xFh8fb7FYmpubMzIybr311pKSEvTqYoxNT0/bbDaNRmMymTZu3AgtfGAMQSYj/GXsOx/e5ubmwKq66aab0CJgaGgICpLU4gC3dW5uDp1oKeovcqp0H/d1hO1aGn9bF0cp6qCIR5FEkLhaGd4Yx37hm5Px9JtQQDzoaQXFyoRB+eu55gCU93YuJzwf9AO8A6AYfSEgJXR1mQqe8sT/roJiRAenlhYky8A4cXrFoS5nIfiEo94rXcuCTv6g7y/ylbCFLWxh+xgNKzxKRVGoSiEtl8vV0dHR0tLCGCsuLl66dGlhYaFKpfJ4PJIkgXaMXUAMrWt+rQyFqn6/32AwlJSUuFwuq9W6c+dOnGdUVBS0RAVBqKioSE1NTUlJoTIGImF7PJ5FBHb+qkyv1yOjgpa3KpUKLatIpRQFCYwxiH273W6FND77SCS2rtWmGeo4nxYf5nqDh0VAaVAT6HlTAGt6LQSo9dN/0f+KXIcFxe+FcgB4fK8InJP7AsPxeY47f3rEWVccNhTnfpHh4D/DX6Dif/lfD7woKkrmDyvItQGB5xP46/gzVA0AJSgUfhHP/eI/wMtdCVxfDBRtY2zpFyVJIjWAwLupyAnwv/7ptVDnr3CKAudVqM9f5vFDWdi7CFvYwnalplarUS8IorzX6yW9yPr6+u9///tDQ0NxcXF79uwxm82VlZWoTRRFEegfeQDir14/E2TBOsYYCEvT09MJCQnY4iFpHxkZ2dbW5vV609PToeqjUqng2KDjFfGIwjY7O6vRaKDCQtkVMl4QicnYRqPRICdA8OmSopYf3i65z16n44ey6+1IBAZ82aXw5zU3/gRCBcrVik+TUfm/sFBdSAHECZjycld8qWioC1a8LywsMwj6GX4oA0E2vbi6IaZhwtXxCZDLOX/6dQXXX3HmgR7FlRpfcKw4IN0L/gO83hGSvJIkoTUdf4+CnvYipnA/Pjl2pQtN4FXw02zxQ33Srj1sYQvbX61RgFwQBLSj8vl8Tz31VHNzc09Pz8qVKzds2LBy5UqdTgdngCJrkqyl8RGcJPrOotrYYDAAjKIzgCRJ6Iel1WpNJlNMTAyiVyS6D+VNfCxcgQaDzo/X64VHNDMzMz09DU8AOz7t8vgToi88fFoc51wrC2cAYFc6Dld6XUHRyyIH+S8KkMQJJDGuKtfPNf3FF+COU8g/EDmxKyzGVZxA0JPmo86XvLarBta8ayHJyY1Qn/cvbE5M/4aK9Ace6uoAtALx0wve6PhY36m7BH9fkArAv5Lc+o4cGN5L4S+QP41PyyN3Oabw1xf/JF5IV1vGHbawhS1s19ZoVcfGhEBPY2PjiRMnOjs7CwoKysrKli1bNjU1FR8fD2FBfJhU52kvuK7nGRUV5fV6DQYDpPQ9Ho9Wq4WMJnAF+ohFR0drtdrZ2VnSQfd4PIwxVDZDHf+6nuenxeDFURsvURQxdKiaoO0efhRjDKWAtH3zaOfjvIywXTvjMdslcVpIFSA8Y3jMyJVknPwlUQzxJ+nTU+oA/4aiAPGFxZeDpQKRKB+6ZhzUvmQEN6hRQwCeYyOG7jvIB/X5qDwvo8kWeiz8ZSrO9vLNv7Bxm+JM+IeZPDcEeCA0q9VqSSdO5NrTkuyp4vIDf4tfONhfShFw2MIWtrB9eg1K3GgqBNzc0dHx+uuvj46OMsZycnJuvfVWRP3R/gVtRyVJIlwoyZnh63qekGYXBAH7EYRKnU4n1EhxAlTsi8shzIp0AZpnXdeT/BTZ5ORkdHQ0Bop285mZGci1I6hH5d2K6GE4lf2XZwJnl4OE/6sGgI8l48UixbvSwhJhxgWbKfxAlHSiDPFokoWmnYWS0Qw1R68U6IeKHIQ6zpXq15IcKguQ6wlqNA6BgfbAiD6Tm5BLstoS8fkIvvu5hmj8PGABlQO4X5IkkYOnGGc+q0AOHu/YCIsmSYLatbqP18qu1dp3pfOHf3bYh3AIwxa2sIWNtuDBwcH09PT+/v5nn332gw8+GB4evv3223fv3p2QkDA/P6/X60VRnJmZud56/2H7y7BQ+5oCMdKfn7SA4MeIN4Li2CvFn1d6nld6vWphIf+HDypfpgU9dCjQecmUxLWy6w3srpUFokC8oKQeH3RnC9n//AcUY0uv4WAovss4GVaFy4Hoi8B1+wo6kvyhwraIXcL/DhOKwha2sH1owwKiVquTkpL6+/s7OztFUWxvb7/33nurqqri4uKYXBIqiuL1DvOHLWxhu652FcyRoO+rr8LDCBrYloJ1HeMjxDx1RAotD3qt7NPiAPAeM/9bij4JEleSQcQ+cqsWIUTyzkPgPSIfIPAdtjAsrQhRBzoPYbsiC9Rfo9TNx3ZOYQtb2D6d5vP5xsbGoqKiPB7PyZMn33333Z6enrq6ut27dycmJkZERCA1ja0kVFgnbGEL26fCrlVmQ03fUeQBFlkgeAgY9OcVeHGRT370dqW/e8kU2HUykev4C7qOxEmyBmYAABwV7oTA1STwlyNwprgoPjjEJxagGsTCoP8KLdRw0aOh8NA+0pMLW9jC9hdhOp0Ojd5PnTp16NChmZmZ5OTkb33rW7GxsVjSVSqVTqfjt5KP+5TD9imw8JZ0be3jeu4uLQN6mabQp+eNB6Z8iJotjDF/NFgn1AV/kic0H8gHxZ93AEjGB29Sta7EFePyV6fIzwSN3PN/KnI7ofxA+vC1vvqwhS1sYQvbVZrf7z9z5szrr7/e2dl57733lpeXo3mW3++HVDwkdMLoP2xh+7TbtcK3av47gS9C/UBQQM+TUpjM/+FBrYJG8rHYxxXRv1JTDC8JDdG/CqHVUOF5xXXRnyTrxBZ6booOwcTUCur4hfeSS9ri8yrQ4/pITipsYQvbX5QNDg5OTk4++uijOp3ue9/7XnZ2ts/ni4yMpH2B1+1Ai4CP+5TD9imwa0U1CRvs48KfIWsAFJWj9GLxEw0K5XkHIJBzQu8vwiC6hnatJu5Hk6kIhOmBZB684/V68b88hycUfFS8GXTkFb7c5dx3eieMWa/aFP0WWJj9H7awhe1qzWq1PvzwwykpKX/3d39XVFTk8/kMBoPb7Y6KikKRGPT+Pxqx/7CFLWzX1a7VI6xWEHKu4riLLCgKcClx+qHXW4bs0+K5KhA84WwAffqMILP/F/k6/w7dVog9864XC50uYIyFqgGgL/JTJbyRXNJCDRHdbv5ehPfmsIUtbFdhbre7sLBwz549UVFRoijqdDqn04maYIT//X4/WECMMTSB+bhPOWxhC9tV2pXi21DPuzA3N6cI0uNfairOS8sLcmMpPp/IE0hYALikYLYk17CiKZ0ihk0fDnUBwsJyST6zGfT8r9UCdxWORNDzvIbvX1fjG4Hxl/NJc5yut10+defqRuZ6z9u/NhO4/iRM5h+GHaqwhe2KLLCx5sd6OmH7hFogMmQfCSX40xLYvVYW6nlEQi+QdEP4IRCHBz1+yCJg6vgrySqTqEANKlsZ3mX/kiyQNRSm+pAFYsrw4IQtbGELW9j+Ci28930spsD3V30XQjoAgPtsIQRUMPg/DO4PesZXcRlBnZDwpLyGFvbuFjEFkydsYQtb2MIWtr9sC0pYCO+D18kCqzQp9s/nXq4CqoV0AIh3zkKo+nz4Ox14DR/ygNfDAs/tL5sSEzTz+8m8NdfVLue+h33OT44ptp8w+SdsYQtb2K638dg0vORe0q6UwhT4PuGQawLV/j/wU0wbuL6OQgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ucNwHh945-D8"
      },
      "source": [
        "## Prepare image for model\n",
        "\n",
        "Next, we use `TrOCRProcessor` (which wraps a `ViTFeatureExtractor` and a `RobertaTokenizer` into one) to resize + normalize the image for the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195,
          "referenced_widgets": [
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            "493ea6c5986141ddbdd7e6e7e77673e5",
            "f10376a01d744e05805f8a1b76608aa5",
            "2ab7b572c2c34bc7b901c6c9082848a8",
            "485344c7385445529f7f5f9f12616ef4",
            "cf2939562c2d4028ac1738d470553881",
            "f7935536c8874409b6c9c061b6cb37c9",
            "d868e3bf58ff40e69a8cae8d6c228f60",
            "3b08aa87c91d47e18b328859ef4b89c8"
          ]
        },
        "id": "ut0BMGQm6Khf",
        "outputId": "75065197-4975-4808-b843-d3a51cf051cf"
      },
      "source": [
        "from transformers import TrOCRProcessor\n",
        "\n",
        "processor = TrOCRProcessor.from_pretrained(\"microsoft/trocr-base-handwritten\")\n",
        "# calling the processor is equivalent to calling the feature extractor\n",
        "pixel_values = processor(image, return_tensors=\"pt\").pixel_values\n",
        "print(pixel_values.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "627bab3808964a598148e17744b7ab91"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6345df73da94d89a1c2033ee6e9a18c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d3e7c122c4c2462c9424432199171054"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4bc2dc0b00814a5d90073f90c8aba6ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "780b633d091c4bd8a441773c130ff2f9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 3, 384, 384])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vFbzOsf16UQS"
      },
      "source": [
        "## Load model\n",
        "\n",
        "Here we load a TrOCR model from the [hub](https://huggingface.co/models?other=trocr). TrOCR models are instances of [`VisionEncoderDecoderModel`](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder), which combine a vision encoder (like ViT, BEiT, DeiT, ...) with a language model as decoder (like BERT, RoBERTa, GPT-2, ...). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "83c00e5e96f541b58c41193273ecc3f4",
            "fa904b81700f4fe8b60b23092ffcd8cb",
            "35d5ee909c53439bb0bfd9014ab38ae3",
            "523bc4ae908d421ea58f11ee5dfc6264",
            "0c075d3dfbc241b1a925e40330f3f315",
            "5241dc6ee0cb469983bcd5b7c04546a7",
            "92eb2886167d45f6b0efea1b52916ae5",
            "93a663efe36a4e06ada8442b597d0076",
            "a540ec88f6e348c38a2529d14cfcfa65",
            "902124e662114511be58d3a4bdea10ce",
            "73a1e24ce1ba4b508a7d59af479ab726",
            "c2d794d1ba7e478cb1a775d9e8868762",
            "5f6622e4a1ea42a08809a9862fd75d66",
            "20cc95412c524167a1e44b5253d9ee9e",
            "5c27ee175c67458fa9d4e794a3aa19f1",
            "2b696e8146714017b75fb6c16809e63b",
            "e0224e3e928a422a9082189fe86aa0a9",
            "73ca010ce17d4ffe9556fdc8d9ccbca3",
            "1cbb5abde3db4997a7a66bb8b30b2324",
            "fb01081a801d4697a75e3ab081e29357",
            "eaf7b69f65204c31b71ead1922525e71",
            "fd08997b6f95449ba30c692705eeafb2"
          ]
        },
        "id": "v5Yf5Fql6U8Y",
        "outputId": "a25ec7e2-01e5-421d-f333-67f9fb63b5ed"
      },
      "source": [
        "from transformers import VisionEncoderDecoderModel\n",
        "\n",
        "model = VisionEncoderDecoderModel.from_pretrained(\"microsoft/trocr-base-handwritten\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "83c00e5e96f541b58c41193273ecc3f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.24G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2d794d1ba7e478cb1a775d9e8868762"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-handwritten and are newly initialized: ['encoder.pooler.dense.weight', 'encoder.pooler.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QKk3c-HG6g87"
      },
      "source": [
        "## Generate text \n",
        "\n",
        "Finally, we can generate text autoregressively using the `.generate()` method. We use the tokenizer part of the `processor` to decode the generated id's back to text. Note that by default, greedy search is used for generation, but there are more fancy methods like beam search and top-k sampling, which are also supported. You can check out [this blog post](https://huggingface.co/blog/how-to-generate) for details."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jrTnzzUUPLP",
        "outputId": "ea9c8c3f-dc02-4e51-a0a1-efcbf4907d9d"
      },
      "source": [
        "generated_ids = model.generate(pixel_values)\n",
        "generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
        "print(generated_text)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "industry, \" Mr. Brown commented icily. \" Let us have a\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P99E4R2vXz9t"
      },
      "source": [
        "## Gradio demo\n",
        "\n",
        "We can also wrap up everything we wrote above in a quick [Gradio](https://gradio.app/) demo. Gradio is an awesome tool for creating quick ML demos!\n",
        "\n",
        "We just need to define a function that takes in an image input and produces a text output (note that Gradio supports many other input & output formats)!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EzbHm95iX0ia",
        "outputId": "c8f67dba-dca9-46a1-d1e7-beed3bb6407d"
      },
      "source": [
        "!pip install -q gradio"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 2.9 MB 7.6 MB/s \n",
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            "\u001b[K     |████████████████████████████████| 58 kB 6.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 10.9 MB 28.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 79 kB 8.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 43 kB 2.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.6 MB 39.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 61 kB 618 kB/s \n",
            "\u001b[K     |████████████████████████████████| 856 kB 60.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 58 kB 6.1 MB/s \n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for python-multipart (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "id": "BQWsUDw7X4z4",
        "outputId": "2b9ede5c-481a-4fcd-9f9e-e6c9e68b1667"
      },
      "source": [
        "import gradio as gr\n",
        "from transformers import TrOCRProcessor, VisionEncoderDecoderModel\n",
        "import requests\n",
        "from PIL import Image\n",
        "\n",
        "processor = TrOCRProcessor.from_pretrained(\"microsoft/trocr-base-handwritten\")\n",
        "model = VisionEncoderDecoderModel.from_pretrained(\"microsoft/trocr-base-handwritten\")\n",
        "\n",
        "# load image examples from the IAM database\n",
        "urls = ['https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg', 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSoolxi9yWGAT5SLZShv8vVd0bz47UWRzQC19fDTeE8GmGv_Rn-PCF1pP1rrUx8kOjA4gg&usqp=CAU',\n",
        "        'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRNYtTuSBpZPV_nkBYPMFwVVD9asZOPgHww4epu9EqWgDmXW--sE2o8og40ZfDGo87j5w&usqp=CAU']\n",
        "for idx, url in enumerate(urls):\n",
        "  image = Image.open(requests.get(url, stream=True).raw)\n",
        "  image.save(f\"image_{idx}.png\")\n",
        "\n",
        "def process_image(image):\n",
        "    # prepare image\n",
        "    pixel_values = processor(image, return_tensors=\"pt\").pixel_values\n",
        "\n",
        "    # generate (no beam search)\n",
        "    generated_ids = model.generate(pixel_values)\n",
        "\n",
        "    # decode\n",
        "    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
        "\n",
        "    return generated_text\n",
        "\n",
        "title = \"Interactive demo: TrOCR\"\n",
        "description = \"Demo for Microsoft's TrOCR, an encoder-decoder model consisting of an image Transformer encoder and a text Transformer decoder for state-of-the-art optical character recognition (OCR) on single-text line images. This particular model is fine-tuned on IAM, a dataset of annotated handwritten images. To use it, simply upload an image or use the example image below and click 'submit'. Results will show up in a few seconds.\"\n",
        "article = \"<p style='text-align: center'><a href='https://arxiv.org/abs/2109.10282'>TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models</a> | <a href='https://github.com/microsoft/unilm/tree/master/trocr'>Github Repo</a></p>\"\n",
        "examples =[[\"image_0.png\"], [\"image_1.png\"], [\"image_2.png\"]]\n",
        "\n",
        "iface = gr.Interface(fn=process_image, \n",
        "                     inputs=gr.inputs.Image(type=\"pil\"), \n",
        "                     outputs=gr.outputs.Textbox(),\n",
        "                     title=title,\n",
        "                     description=description,\n",
        "                     article=article,\n",
        "                     examples=examples)\n",
        "iface.launch(debug=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-handwritten and are newly initialized: ['encoder.pooler.dense.weight', 'encoder.pooler.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://46686.gradio.app\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.IFrame at 0x7f1a09b17650>"
            ],
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"900\"\n",
              "            height=\"500\"\n",
              "            src=\"https://46686.gradio.app\"\n",
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              "        ></iframe>\n",
              "        "
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0cH-TfbIZS2L"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}